Article
Environmental Sciences
Farhan Mustafa, Huijuan Wang, Lingbing Bu, Qin Wang, Muhammad Shahzaman, Muhammad Bilal, Minqiang Zhou, Rashid Iqbal, Rana Waqar Aslam, Md. Arfan Ali, Zhongfeng Qiu
Summary: In this study, aircraft measurements were conducted to validate the accuracy of CO2 retrievals from two satellites. The comparison with in situ measurements and model datasets showed good agreement among the datasets. The biases in dry-air column-averaged CO2 mole fractions retrieved from OCO-2 and GOSAT were also reported.
Article
Meteorology & Atmospheric Sciences
Stefan Noeel, Maximilian Reuter, Michael Buchwitz, Jakob Borchardt, Michael Hilker, Heinrich Bovensmann, John P. Burrows, Antonio Di Noia, Hiroshi Suto, Yukio Yoshida, Matthias Buschmann, Nicholas M. Deutscher, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Rigel Kivi, Isamu Morino, Justus Notholt, Hirofumi Ohyama, Christof Petri, James R. Podolske, David F. Pollard, Mahesh Kumar Sha, Kei Shiomi, Ralf Sussmann, Yao Te, Voltaire A. Velazco, Thorsten Warneke
Summary: The FOCAL algorithm has been successfully applied to derive CO2 data from GOSAT and GOSAT-2 radiances, showing promising results. By utilizing both S- and P-polarisation spectra in the retrieval, the new application allows for the derivation of various atmospheric constituents beyond CO2. The accuracy of FOCAL and the preliminary comparisons with other data sets suggest its potential as a reliable tool for monitoring greenhouse gas concentrations.
ATMOSPHERIC MEASUREMENT TECHNIQUES
(2021)
Article
Environmental Sciences
Yuanyuan Chen, Jiefeng Cheng, Xiaodong Song, Shuo Liu, Yuan Sun, Dajiang Yu, Shuangxi Fang
Summary: This study applies triple collocation (TC) techniques to evaluate the performances of multiple CO2 products from different satellites and models. The TC-based evaluation results are consistent with the direct evaluation results, showing that CT2019B performs best, followed by OCO-2 and GOSAT. The TC correlation coefficient estimates are more consistent and robust than root mean square error estimates. TC-based error estimates reveal that terrestrial areas generally have larger errors than marine areas, especially for GOSAT and CT2019B datasets. OCO-2 performs well in areas with large errors in CT2019B or GOSAT, such as most of China and Russia.
Article
Environmental Sciences
Chiranjit Das, Ravi Kumar Kunchala, Naveen Chandra, Abha Chhabra, Mehul R. Pandya
Summary: India is interested in understanding the response of regional carbon source-sink to changes in atmospheric CO2 concentrations or anthropogenic emissions. Recent advancements in high-resolution satellite measurements have provided unprecedented details on source-sink activity. The study compared the XCO2 measurements from GOSAT and OCO-2 satellites, finding that OCO-2 has better resolution and can capture local-scale variability more effectively.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Environmental Sciences
Qinwei Zhang, Mingqi Li, Maohua Wang, Arthur Paul Mizzi, Yongjian Huang, Chong Wei, Jiuping Jin, Qianrong Gu
Summary: A regional CO2 flux inversion system was developed to invert the CO2 flux over the contiguous United States in 2016, showing good agreement with other datasets. The study demonstrated the potential of combining satellite observations with high spatial resolution CO2 flux inversion system in supporting the global stocktake.
Article
Environmental Sciences
S. Allahudheen, A. Bhuvana Chandra, Rabindra K. Nayak, V. K. Dadhwal, M. Krishnapriya, M. V. Lakshmaiah
Summary: A nested GEOS-Chem atmosphere transport model was used to study the variability of tropospheric CO2 over India and surrounding oceans during 2012-2020. The model was constrained by CO2 influxes from GEOS-Chem global simulations and surface fluxes of CO2 based on historical databases. Model simulations were evaluated using in situ measurements and satellite retrievals. Additional model runs were made to evaluate the role of different factors in controlling the seasonal oscillations of tropospheric CO2.
ATMOSPHERIC ENVIRONMENT
(2023)
Article
Meteorology & Atmospheric Sciences
Steven T. Massie, Heather Cronk, Aronne Merrelli, Christopher O'Dell, K. Sebastian Schmidt, Hong Chen, David Baker
Summary: The presence of 3D cloud radiative effects in OCO-2 retrievals was demonstrated through an analysis of various data sets. 3D radiative perturbations from clouds were found to have a significant impact on XCO2 measurements, and specific metrics were used to quantify and mitigate these effects. Utilizing table lookup techniques based on cloud distance and spatial radiance heterogeneity metrics reduced the 3D cloud biases and improved XCO2bc-TCCON averages.
ATMOSPHERIC MEASUREMENT TECHNIQUES
(2021)
Article
Environmental Sciences
Shaoqing Zhang, Liping Lei, Mengya Sheng, Hao Song, Luman Li, Kaiyuan Guo, Caihong Ma, Liangyun Liu, Zhaocheng Zeng
Summary: This study evaluates the consistency and uncertainty of four commonly used emission inventories in China using satellite observations and machine learning methods. The results show significant inconsistencies among these inventories, especially in areas with high emissions. The inventories tend to overestimate emissions in Chinese cities and slightly underestimate emissions in the USA. The study also finds that the uncertainty of emission inventories is higher in Asian countries compared to Europe and the USA.
Article
Environmental Sciences
Mengya Sheng, Liping Lei, Zhao-Cheng Zeng, Weiqiang Rao, Shaoqing Zhang
Summary: This study investigated the impact of global CO2 emissions on climate change using satellite data, showing an increase in XCO2 related to anthropogenic emissions and seasonal burning. The research found that XCO2 anomalies increase significantly in winter and are associated with regional CO2 emissions.
Article
Environmental Sciences
Jinhui Zheng, Huifang Zhang, Shuai Zhang
Summary: This study verifies the consistency of atmospheric CO2 measurement data from different carbon observation satellites. The results show a strong correlation between the measurements of different satellites, with higher consistency in Asia, North America, and Oceania compared to Europe, South America, and Africa. Seasonal analysis reveals higher consistency in spring and lower consistency in summer.
Article
Meteorology & Atmospheric Sciences
Leslie David, Francois-Marie Breon, Frederic Chevallier
Summary: The article introduces an alternative approach based on artificial neural networks for measuring the mole fraction of CO2 and surface pressure in the atmosphere. The study shows that using this method can achieve better accuracy than the full-physics algorithm.
ATMOSPHERIC MEASUREMENT TECHNIQUES
(2021)
Article
Geosciences, Multidisciplinary
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Gregoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, Thomas Lauvaux
Summary: Under the Copernicus programme, an operational CO2 Monitoring Verification and Support system (CO2MVS) is being developed to exploit data from future satellites monitoring the distribution of CO2 within the atmosphere. This study investigates the potential of deep learning methods, particularly convolutional neural networks, to identify plume-specific spatial features in satellite images for accurate estimation of local CO2 emissions from cities or power plants.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2023)
Article
Meteorology & Atmospheric Sciences
Steffen Mauceri, Steven Massie, Sebastian Schmidt
Summary: The OCO-2 satellite measures radiance in different bands to estimate column-averaged atmospheric CO2 dry-air mole fractions (XCO2). However, calibration issues and mismatches between the retrieval algorithm and observed radiances cause biases in the retrieved XCO2 values. Multiple linear regression is used to mitigate these biases. A bias correction model based on interpretable non-linear machine learning is developed to address 3D cloud biases, reducing unphysical variability over land and sea. The approach is applicable to other greenhouse gas experiments.
ATMOSPHERIC MEASUREMENT TECHNIQUES
(2023)
Article
Environmental Sciences
Yutong Jiang, Zekun Gao, Junyu He, Jiaping Wu, George Christakos
Summary: In this study, the Data Interpolation Empirical Orthogonal Function (DINEOF) and Bayesian Maximum Entropy (BME) methods were combined to interpolate the carbon dioxide (CO2) concentration in the ocean. The synthetic DINEOF-BME approach showed better performance than the DINEOF method alone, with higher accuracy in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Bias.
Article
Geosciences, Multidisciplinary
Changpei He, Mingrui Ji, Tao Li, Xinyi Liu, Die Tang, Shifu Zhang, Yuzhou Luo, Michael L. Grieneisen, Zihang Zhou, Yu Zhan
Summary: Due to coarse spatial resolution, the CarbonTracker's XCO2 data fails to capture the spatial heterogeneity of XCO2. In this study, a machine learning model was developed to fill the data gaps in the Orbiting Carbon Observatory 2 satellite retrievals in China, yielding high cross-validation results. The filled data set revealed regional variations in XCO2, with the highest average in East China and the lowest in Northwest China.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Geography, Physical
Shuai Xie, Liangyun Liu, Xiao Zhang, Jiangning Yang
Summary: Monitoring land cover changes at high temporal frequencies using dense observations provides more comprehensive understanding compared to change detection using two-dates satellite images. This study combined the Continuous Change Detection and Classification algorithm with the Markov random field model to explore the annual dynamics of land cover in Beijing from 2001 to 2020. The proposed method generated consistent and accurate land cover maps, revealing the urban expansion and replacement of cultivated land in Beijing over the past two decades.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Forestry
Siyuan Chen, Liangyun Liu, Lichun Sui, Xinjie Liu
Summary: Partitioning non-photosynthetic components is important for accurate estimation of gross primary production in deciduous forests. The use of vegetation indices and radiation absorption observations can effectively separate the radiation absorbed by photosynthetic components. The results of this study demonstrate that the APAR(green)-based method significantly improves daily GPP estimation.
JOURNAL OF FORESTRY RESEARCH
(2023)
Article
Geochemistry & Geophysics
Lisheng Song, Zhonghao Ding, William P. Kustas, Wei Hua, Xinjie Liu, Liangyun Liu, Shaomin Liu, Mingguo Ma, Yan Bai, Ziwei Xu
Summary: Quantifying and monitoring land surface evapotranspiration (ET) is crucial for understanding the earth's water, energy, and carbon cycles. This study developed an empirical relationship between sun-induced chlorophyll fluorescence (SIF) and plant transpiration (T) at ecosystem scale, and coupled it with the two-source energy balance model (TSEB-SIF) to estimate ET components. The TSEB-SIF model outperformed the TSEB model in estimating ET, particularly under water deficit conditions, and provided more accurate partitioning of T from ET.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Environmental Sciences
Xiao-Xiao Zhang, Jia-Qiang Lei, Shi-Xin Wu, Sheng-Yu Li, Lian-You Liu, Zi-Fa Wang, Shuang-Yan Huang, Yu-Hong Guo, Yong-Dong Wang, Xiao Tang, Jie Zhou
Summary: Aeolian dust in China has been decreasing gradually since 1984, with the frequency of dust storms declining significantly by 97.7%. The main dust sources influencing China are the Mongolian Gobi Desert, the Taklimakan Desert, the Hexi Corridor, the Alxa Plateau Desert, the Qaidam basin Desert, and the northeast-southwest stretching semiarid farming-pasture ecotone. The Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) may play important roles in the occurrence of aeolian dust events in China.
LAND DEGRADATION & DEVELOPMENT
(2023)
Article
Environmental Sciences
Xiaojin Qian, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen, Qi Sun
Summary: In this study, a global 500 m leaf chlorophyll content (LCC) weekly dataset (GLCC) was generated using satellite data and a physical radiative transfer modeling approach. The GLCC dataset facilitates vegetation growth monitoring and terrestrial carbon cycle modeling. The dataset was validated using field measurements and showed good consistency with the existing MERIS LCC dataset.
Article
Endocrinology & Metabolism
Yuxia Zhou, Lu Liu, Bangming Jin, Yixuan Wu, Lifen Xu, Xuebing Chang, Laying Hu, Guifang Wang, Yali Huang, Lingyu Song, Tian Zhang, Yuanyuan Wang, Ying Xiao, Fan Zhang, Mingjun Shi, Lingling Liu, Tuanlao Wang, Rui Yan, Bing Guo
Summary: Ectopic lipid accumulation in renal tubules is closely related to the pathogenesis of diabetic kidney disease, and mitochondrial dysfunction is thought to play a key role. This study shows that the Metrnl gene product mediates lipid accumulation in the kidney and has therapeutic potential for DKD.
Article
Environmental Sciences
Shanshan Du, Xinjie Liu, Jidai Chen, Weina Duan, Liangyun Liu
Summary: In recent years, several satellite-based solar-induced chlorophyll fluorescence (SIF) products have emerged, but direct validation of these products has not been conducted. This study validated two groups of TROPOMI SIF products using tower-based SIF measurements at seven sites. Challenges such as spatial scale mismatch were addressed using a near-infrared reflectance of vegetation (NIRv)-scaled approach. The validation results showed differences in the performance of the SIF datasets and highlighted unresolved issues in SIF product quality.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Environmental Sciences
Tingting Zhao, Xiao Zhang, Yuan Gao, Jun Mi, Wendi Liu, Jinqing Wang, Mihang Jiang, Liangyun Liu
Summary: In the past decades, several fine-resolution global land cover (GLC) products have been developed with the development of computing capacity and free access to imagery. However, there is a lack of consistency analysis or comprehensive accuracy assessment using a common validation dataset for these GLC products. In this study, a novel stratified random sampling GLC validation dataset (SRS_Val) was developed, and the accuracy of six GLC products was quantitatively assessed. The results revealed variations in accuracy and consistency among the GLC products.
Article
Environmental Sciences
Tiewei Li, Lanlan Guo, Bin He, Lianyou Liu, Wenping Yuan, Xiuzhi Chen, Xingming Hao, Xuebang Liu, Hao Zheng, Huan Zheng, Rui Wang
Summary: More frequent droughts are changing the European forest ecosystem, impacting the global carbon cycle. Tree height plays a role in forests' resistance to drought, with taller forests showing higher resistance and shorter forests experiencing larger negative effects. The capacity for water absorption and regulation differs between forests of different heights, contributing to these resistance differences. This research provides insight into tree-level function and its implications for future droughts.
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
(2023)
Article
Environmental Studies
Hong Jia, Rui Wang, Hang Li, Baijian Diao, Hao Zheng, Lanlan Guo, Lianyou Liu, Jifu Liu
Summary: Desertification in the Gonghe Basin has been effectively controlled through remote sensing monitoring and the Albedo-NDVI feature space method. From 2010 to 2020, the desertification situation improved, especially in the western part of the basin where desertification land area decreased by 827.46 km(2) and desertification intensity decreased. The changes in desertification were influenced by both natural and human factors, with human activities gradually playing a larger role in desertification reversal.
Article
Agronomy
Peiqi Yang, Xinjie Liu, Zhigang Liu, Christiaan van der Tol, Liangyun Liu
Summary: This study examines the relationship between sun-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) and the factors influencing this relationship. The results show that the radiative and structural components of SIF have a positive impact on the linearity of the SIF-GPP relationship, while the physiological component has a negative impact. The controls of the relationship also vary with temporal scale.
AGRICULTURAL AND FOREST METEOROLOGY
(2023)
Article
Environmental Sciences
Mengjia Qi, Xinjie Liu, Shanshan Du, Linlin Guan, Ruonan Chen, Liangyun Liu
Summary: This study emphasizes the importance of considering soil reflectance in estimating fluorescence escape probability (fesc) for downscaling solar-induced chlorophyll fluorescence (SIF) from canopy level to leaf level. The proposed fesc_GPR-SR model, accounting for soil reflectance, outperforms the traditional NIRv/FAPAR model in estimating fesc, especially for sparse vegetation. The evaluation results indicate that the leaf-level SIF calculated by the fesc_GPR-SR model tracks better with absorbed photosynthetic active radiation by green components (APARgreen). Therefore, accounting for soil reflectance can contribute to a better understanding of the physiological mechanism between SIF and gross primary productivity (GPP).
Article
Geosciences, Multidisciplinary
Xiao Zhang, Liangyun Liu, Tingting Zhao, Xidong Chen, Shangrong Lin, Jinqing Wang, Jun Mi, Wendi Liu
Summary: This study proposes a novel method for wetland mapping by combining multiple data sources and satellite images. The method successfully generated a global 30 m wetland map with a fine classification system, including inland and coastal wetland sub-categories. The resulting dataset showed significant advantages in capturing spatial patterns and diversity of wetland sub-categories compared to other global wetland products.
EARTH SYSTEM SCIENCE DATA
(2023)
Article
Chemistry, Analytical
Lieshen Yan, Xinjie Liu, Xia Jing, Liying Geng, Tao Che, Liangyun Liu
Summary: This study proposes a multi-angular NDVI (MAVI) method to enhance the estimation of leaf area index (LAI) by reducing interference from soil background and saturation effects. The MAVI is evaluated against other vegetation indices and shown to have improved performance in LAI retrieval. The findings demonstrate the utility of tower-based multi-angular spectral observations in LAI estimation and expand the potential applications of multi-angular observations.
Article
Environmental Sciences
Xiao Zhang, Liangyun Liu, Jinqing Wang, Tingting Zhao, Wendi Liu, Xidong Chen
Summary: A novel method for automated global tidal flat mapping was proposed using Google Earth Engine, resulting in the first global tidal flat dataset covering high latitudes. The method achieved high mapping accuracy and covered the entire global coastline. The dataset provides valuable information for coastal ecosystem protection and supports coastal development. The study also compared the dataset with existing ones, demonstrating significant improvement in mapping accuracy.
JOURNAL OF REMOTE SENSING
(2023)
