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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
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Environmental Sciences
Helin Zhang et al.
Summary: This study generated a global GPP dataset based on an improved LUE model, considering temperature, water, atmospheric CO2 concentrations, radiation components, and nitrogen index. The dataset showed good spatial consistency and provides a reliable alternative for large-scale carbon cycle research and long-term GPP monitoring.
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Mathematics
Jianyi Lyu et al.
Summary: Long non-coding RNAs (lncRNA) are RNA transcripts with more than 200 nucleotide residues. They play versatile roles in cellular processes and their subcellular localization is closely associated with their function. This article presents a computational predictor called LightGBM-LncLoc, which utilizes reverse complement k-mer and position-specific trinucleotide propensity to encode lncRNAs and achieves state-of-the-art performance in recognizing lncRNA subcellular localization.
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Telecommunications
Yinliang Qiu et al.
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CHINA COMMUNICATIONS
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Enqing Hou et al.
Summary: This study used a matrix approach to analyze the differences in simulating land carbon dynamics between different models and identify their sources. The study found that the differences between models mainly came from differences in baseline carbon residence time and environmental factors, and these differences can be reduced by standardizing model parameters. The findings of this study are important for improving climate change prediction.
GLOBAL CHANGE BIOLOGY
(2023)
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Forestry
Wei Shangguan et al.
Summary: A global carbon fluxes dataset (GCFD) has been developed using a deep learning convolutional neural network (CNN) model. The dataset includes gross primary productivity (GPP), terrestrial ecosystem respiration (RECO), and net ecosystem exchange (NEE) at a spatial resolution of 1 km and three time steps per month from January 1999 to June 2020. The CNN model outperformed other machine learning methods and showed higher accuracy and more spatial details compared to other global carbon flux datasets. GCFD can be a valuable reference for meteorological and ecological analyses and modeling.
Article
Environmental Sciences
Lei Xu et al.
Summary: Ocean primary productivity is crucial for ocean ecosystems and the carbon cycle. Accurate forecasting of ocean primary productivity months in advance is beneficial for marine management. This study proposes a joint forecasting model that combines seasonal climate predictions and temporal memories of relevant factors to examine the predictability of ocean productivity. The results show that the combination of seasonal SST predictions and local memory can skillfully predict a large portion of productive oceans at different lead times. The hybrid data-driven and model-driven approach improves the predictability of ocean productivity, with seasonal climate predictions playing a significant role.
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Chemistry, Multidisciplinary
Qinmeng Yang et al.
Summary: This study developed a site-level GPP prediction method based on the GeoMAN model, which was able to extract spatiotemporal features and fuse external environmental factors to predict GPP on the Tibetan Plateau. The GeoMAN model achieved the best results in predicting GPP at nine flux observation sites, with an accuracy of R-2 = 0.870, RMSE = 0.788 g Cm-2 d(-1), and MAE = 0.440 g Cm-2 d(-1).
APPLIED SCIENCES-BASEL
(2023)
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Environmental Sciences
Zhenkun Tian et al.
Summary: Through the comparison and fusion of eight ecosystems, we found that the EC-LUE model was more accurate in estimating CO2 uptake than other models. Additionally, random forest and support vector machine algorithms performed better in merging different models. Based on the individual models, the fusion methods of Bayesian model averaging, support vector machine, and random forest improved the average accuracy of estimation by 8%, 18%, and 19% respectively.
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
(2023)
Article
Environmental Sciences
Renjie Guo et al.
Summary: In this study, a machine learning model (random forest) was used to establish a global GPP data set named ECGC_GPP. The model distinguished nine functional plant types and estimated monthly GPP data from 1999 to 2019. The results showed a significant contribution of LAI to the monthly variation of GPP, and an upward trend in annual GPP during the study period. The use of plant functional type classification improved the estimation accuracy of cropland GPP.
JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES
(2023)
Article
Oceanography
Jens M. Nielsen et al.
Summary: This study quantifies primary production rates in the southeastern Bering Sea from 2016 to 2019 and finds that the majority of gross primary production (GPP) and net community production occur during the spring phytoplankton bloom. After the bloom, the water column experiences low GPP and net biological carbon consumption. Phytoplankton growth rates are commonly suppressed in late summer due to nitrogen limitation. This research provides important insights into seasonal variations of biogeochemical cycles, phytoplankton community growth rates, and carbon availability in the southeastern Bering Sea using high-temporal-resolution measurements.
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS
(2023)
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Plant Sciences
Jianqiang Yang et al.
Summary: Anthropogenic disturbance, such as nitrogen fertilization and mowing, is constantly changing the function and structure of grassland ecosystems, and will continue to affect their sustainability. However, the effects of different nitrogen addition levels and frequencies, as well as mowing, on nitrogen cycling processes are still unclear.
Article
Limnology
Pascal Perolo et al.
Summary: In alkaline freshwater systems, bicarbonates can support gross primary production (GPP) even at low CO2 concentrations. However, the contribution of bicarbonates to GPP in lakes has not been quantified throughout the seasons. This study analyzes the daily stoichiometric ratios of CO2-O-2 and alkalinity-O-2 in a deep hardwater lake, revealing that alkalinity is the dominant inorganic carbon source for GPP in both littoral and pelagic environments during the stratified period.
LIMNOLOGY AND OCEANOGRAPHY LETTERS
(2023)
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Environmental Sciences
Renato K. Braghiere et al.
Summary: Estimating the impacts of climate change on the global carbon cycle relies on projections from Earth system models (ESMs). The new generation of increased complexity ESMs in the Intergovernmental Panel on Climate Change Sixth Assessment Report aims to improve future climate projections. In this study, CMIP5 and CMIP6 ensembles were benchmarked using ILAMB tool over the NASA Arctic-Boreal vulnerability experiment (ABoVE) region in North America, showing that CMIP6 has higher projected average net biome production (NBP) in 2100 compared to CMIP5, and better agreement with contemporary observed carbon cycle variables.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
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Optics
V. M. Domysheva et al.
Summary: The gross and net primary production of the characteristic seasonal life cycles of Baikal plankton are estimated based on long-term measurements of carbon dioxide concentrations and fluxes in the near-water atmosphere and surface and bottom water. Methodological issues in the coastal zone are analyzed to avoid significant uncertainty in these characteristics. It is shown that net primary production cannot be accurately estimated solely from CO2 concentration measurements. The productivity estimates for different periods are consistent with long-term observations.
ATMOSPHERIC AND OCEANIC OPTICS
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Agronomy
Xiaobin Guan et al.
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AGRICULTURAL AND FOREST METEOROLOGY
(2022)
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Geography, Physical
Chengcheng Gang et al.
Summary: The study evaluated the spatiotemporal dynamics of terrestrial CUE based on MODIS data from 2000-2018, showing a significant global increase in vegetation CUE primarily driven by a more rapid increase in NPP compared to GPP. Different regions experienced varying effects of climate change on vegetation CUE, emphasizing the need for flexible ecosystem management to adapt to future warming climates.
GLOBAL AND PLANETARY CHANGE
(2022)
Editorial Material
Biodiversity Conservation
David J. P. Moore
Summary: Incorporating ecological processes into Earth System Models can likely enhance their long-term performance, prompting ecologists to test complex ecological hypotheses on regional and global scales. Some candidate processes have been suggested for inclusion.
GLOBAL CHANGE BIOLOGY
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David Martini et al.
Summary: The study investigated the impact of the 2018 European heatwave on the GPP-SIF relationship in evergreen broadleaved trees, revealing an inversion of the photosynthesis-fluorescence relationship due to extreme heat stress and changes in energy dissipation pathways.
Review
Biology
Yuanhe Yang et al.
Summary: Enhancing the terrestrial ecosystem carbon sink is crucial for slowing down the increase in atmospheric CO2 concentration and achieving carbon neutrality. This review summarizes the progress in terrestrial C budget researches, clarifies spatial patterns and drivers of terrestrial C sources and sinks, and examines the role of terrestrial C sinks in achieving carbon neutrality.
SCIENCE CHINA-LIFE SCIENCES
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Ecology
Peixin Yu et al.
Summary: This study explores the impact of water stress on gross primary productivity (GPP) caused by global warming. By using a convolutional neural network (CNN) model, the researchers consider the water stress indicators at different timescales, GPP observations, and remote sensing-based indexes. The optimal CNN model effectively simulates the spatial patterns and interannual variations of GPP.
REMOTE SENSING IN ECOLOGY AND CONSERVATION
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Astronomy & Astrophysics
Junchao Liang et al.
Summary: In this paper, a new method using PCA and LightGBM algorithms to estimate stellar atmospheric parameters is proposed, and experimental results show that this method outperforms other methods. It is also found that the new features obtained by PCA can solve the problems caused by direct use of original photometry data or color index data.
ASTRONOMICAL JOURNAL
(2022)
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Biodiversity Conservation
Han Yan et al.
Summary: Extensive grassland degradation poses a threat to ecological security due to climate change and intensified human activities. Predicting grassland degradation on a large scale is challenging due to its complexity. The emergence of machine learning algorithms provides a potential solution. In this study, random forest and neural network models were used to predict grassland degradation. The random forest model achieved high prediction precision, while the neural network model did not. Geographic, meteorological, and plant variables explained 61.8% of the total variance, which was increased to 72.8% by microbial markers.
ECOLOGICAL INDICATORS
(2022)
Article
Biodiversity Conservation
Xiaonan Wei et al.
Summary: This study investigates the lagged and cumulative effects of drought on global grassland gross primary production (GPP) using remote sensing data and climate indices. The results show that the majority of grasslands globally exhibit lagged responses to drought, with a typical lag time of one month. Cumulative effects of drought on grassland GPP are widespread, and relatively arid areas show stronger cumulative effects. The study highlights the importance of considering the cumulative effect of drought on grassland productivity, which surpasses the lagged effect.
ECOLOGICAL INDICATORS
(2022)
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Ecology
Fangyi Zhang et al.
Summary: The study found that there are differences in simulated terrestrial carbon budgets driven by ERA-Interim and ERA5, with significant discrepancies in Gross Primary Productivity (GPP) in regions such as the Amazon Basin, Congo Basin, and South Asia. These differences are mainly caused by less precipitation and higher temperature in ERA5 compared to ERA-Interim, resulting in a reduction in GPP. This highlights the challenges in using ERA5 and ERA-Interim to evaluate ecosystem responses to climate change.
ECOLOGICAL INFORMATICS
(2022)
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Environmental Sciences
Fang Liu et al.
Summary: This study analyzed the cropland gross primary production (GPP) and related factors in China, revealing a mixed trend of continuous increase, stagnation, or decline in GPP. The spatial mismatch between crop production and water availability has worsened, posing challenges for sustainable management and food security.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
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Ecology
Mohammed Hamdan et al.
Summary: This study found that benthic microalgal GPP can be CO2-limited when light is not limiting, and both dissolved inorganic carbon and DOC additions can stimulate benthic GPP.
FRESHWATER BIOLOGY
(2022)
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Environmental Sciences
Xiaojuan Huang et al.
Summary: This study demonstrates the advantages of using high spatial resolution remote sensing data to improve the performance of the revised EC-LUE model in simulating vegetation gross primary productivity (GPP). The results show that matching Landsat data with the flux tower footprint can significantly improve the model performance, particularly in areas with high landscape heterogeneity.
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Environmental Sciences
Weiqing Zhao et al.
Summary: This study explores the relative performance of different vegetation indices in predicting gross primary productivity (GPP) and investigates additional factors that can better reveal the photosynthetic capacity of vegetation. The results show that solar-induced chlorophyll fluorescence (SIF) performs best when modeled using a single vegetation index, while NIRv combined with CO2, plant traits, and climatic factors achieves the highest prediction accuracy.
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Environmental Sciences
Linqi Liu et al.
Summary: This study evaluates the applicability of light use efficiency (LUE) models in simulating gross primary production (GPP) of a cork oak plantation. It is found that water stress has a greater impact on the model's performance than temperature stress. The modified LUE models significantly improve the prediction accuracy of GPP, explaining 49-65% of the daily GPP variation. On cloudy days, the performance of the modified models does not improve, and the evaporative fraction is more suitable for defining the water stress scalar in the models.
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Environmental Sciences
Yunfeng Hu et al.
Summary: This study compares and comprehensively evaluates four spatiotemporal fusion models for NDVI generation, and finds that the GF-SG model performs the best with the highest comprehensive trade-off score, capable of producing high-resolution NDVI images.
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Chemistry, Multidisciplinary
Youngki Park et al.
Summary: This paper presents a novel programming environment where students can easily train machine learning models based on large-scale data using block-based programming language. The experimental results show that teachers and pre-service teachers give high scores on all evaluation measures for this environment.
APPLIED SCIENCES-BASEL
(2022)
Review
Environmental Sciences
Zhaobin Wang et al.
Summary: The paper provides a comprehensive review of the application of remote sensing technology in grassland monitoring and management. It discusses the estimation methods for various grassland parameters and reviews the applications of remote sensing monitoring, including grassland degradation, grassland use, disaster monitoring, and carbon cycle monitoring. The study suggests that advanced estimation methods and deep learning should be explored in future research.
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Agronomy
Jia Bai et al.
Summary: Previous studies have shown that GPP and SIF have a strong linear relationship and exhibit similar spatial and temporal patterns. However, their responses to the environment may differ. To investigate the impact of the dynamics in GPP-SIF relationship on GPP estimation, two GPP models were established. Considering the variations of GPP-SIF relationship can improve GPP simulation to a certain extent, but the performance of one model is not as good as the other due to associated uncertainties.
AGRICULTURAL AND FOREST METEOROLOGY
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Ecology
Deep Prakash Sarkar et al.
Summary: A machine learning based GPP estimation model utilizing remote sensing, meteorological, and topographical data was proposed, achieving high accuracy in predicting GPP for different plant function types. The model outperformed state-of-the-art machine learning models and showed improved performance when combining different feature sets. It holds promise for simulating GPP under different climate scenarios in the future.
ECOLOGICAL INFORMATICS
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Biochemical Research Methods
Debopriya Ghosh et al.
Summary: Enriched Random Forest is developed to enhance the performance of traditional random forest by reducing the contribution of less informative features. It improves the prediction accuracy, especially when relevant features are few.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
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Environmental Sciences
Zhengnan Gu et al.
Summary: Vegetation cover is crucial for the stability of regional ecosystems. This study used MOD13Q1 enhanced vegetation index data to analyze the trends in vegetation cover in Anhui Province, China from 2000 to 2020. The study also examined the impact of land-use change and human activity on vegetation dynamics.
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Economics
Jesson A. Pagaduan
Summary: This article explores the use of satellite data, including nighttime lights (NTL) data and other remotely sensed data, in predicting subnational GDP in urban and rural sectors of the Philippines. The results show that the higher-quality VIIRS NTL data perform well in predicting urban economic activity, but not rural economic activity. However, incorporating croplands net primary productivity (NPP) as a measure of agricultural productivity significantly improves the performance of land cover as a proxy.
ASIAN ECONOMIC JOURNAL
(2022)
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Geochemistry & Geophysics
Huanfeng Shen et al.
Summary: This article proposes a spatiotemporal constrained light gradient boosting machine model (ST-LGBM) to improve the accuracy of SIF data reconstruction. By considering the data distribution characteristics and introducing two spatial and temporal constraining factors, the accuracy in the missing data areas is significantly improved.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Bruno Montibeller et al.
Summary: This study analyzed the monthly trends in gross primary productivity and evapotranspiration in undisturbed core forest areas in Europe. The results showed that increases in productivity during spring and autumn led to improved water-use efficiency, but these increases were not enough to compensate for decreases in summer. Overall, around 20% of forest areas exhibited a net decrease in productivity during summer.
COMMUNICATIONS EARTH & ENVIRONMENT
(2022)
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Environmental Studies
Hao Wang et al.
Summary: This study evaluated multiple land use/land cover datasets in the Indochina Peninsula and found differences in their consistency and accuracy. The findings provide a reference for data selection in land use studies in the region and reliability assessment of multi-source land use/land cover datasets in other areas.
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Computer Science, Information Systems
Xiaona Chen et al.
Summary: The study focused on the Gross Primary Productivity (GPP) over the Mongolian Plateau from 2001 to 2018, attributing changes to factors such as land-surface temperature, precipitation, landcover change, and atmospheric carbon dioxide concentrations. The 18-year average GPP in the region was 357.02 +/- 24.76 gC m(-2) yr(-1), with forests showing the highest values. The findings suggest that GPP is positively influenced by temperature and precipitation, but negatively impacted by CO2 concentrations and landcover change.
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Environmental Sciences
Yibin Huang et al.
Summary: Global oceanic gross primary production (GPP) has been studied using two satellite-based models, showing that GPP(17 Delta) is 1.5-2.2 times larger than oceanic net primary production (NPP) and comparable to land GPP. The discrepancy between GPP(17 Delta) and GPP(LD) simulations can be partially explained by methodological biases.
GLOBAL BIOGEOCHEMICAL CYCLES
(2021)
Article
Environmental Sciences
Armineh Barkhordarian et al.
Summary: The relationship between tropical atmospheric aridity and global CO2 growth rate is significant, with observed sensitivities indicating an increase in this relationship in the 21st century. Physical mechanisms are responsible for the changes in sensitivities, independent of temperature. Observational evidence suggests that tropical atmospheric aridity is linked to water deficit and evaporative fraction, indirectly driving changes in the carbon cycle.
ENVIRONMENTAL RESEARCH LETTERS
(2021)
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Environmental Sciences
Junxiong Zhou et al.
Summary: Various spatiotemporal fusion methods were compared in terms of their performance under different influential factors, such as geometric misregistration and radiometric inconsistency, to determine the most suitable algorithms for blending NDVI imagery. The study findings provide guidance for users in selecting appropriate remote sensing data processing methods.
REMOTE SENSING OF ENVIRONMENT
(2021)
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Biology
Yuanfeng Sun et al.
Summary: The study reveals that precipitation, temperature, and evapotranspiration play vital roles in shaping the patterns of aboveground NPP and its partitioning to belowground, with belowground NPP patterns not mirroring those of the aboveground NPP. Despite positive correlations of aboveground NPP and total NPP with mean annual temperature, the fraction of belowground NPP is negatively correlated with it. The relationship between belowground NPP and climatic factors is considerably weak, indicating relative stability regardless of climate conditions.
SCIENCE CHINA-LIFE SCIENCES
(2021)
Article
Environmental Sciences
Xiufang Zhu et al.
Summary: Heat and drought stress are major environmental factors limiting vegetation survival and growth. This study analyzed trends in temperature and precipitation indices, as well as the response of GPP to heat and drought in various vegetation regions of China. Results showed increased probability of drastic GPP reduction with higher drought levels and heat intensities, with temperate grasslands and warm temperate deciduous broad-leaved forests being the most sensitive regions in China.
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Engineering, Civil
Chen Zhang et al.
Summary: This study developed a reliable model through machine learning to estimate grassland ecosystem evapotranspiration by combining different data sources, overcoming the challenge of scarce actual measurements. Meteorological variables, vegetation, and soil conditions were found to be the main factors influencing grassland evapotranspiration.
JOURNAL OF HYDROLOGY
(2021)
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Engineering, Civil
Zhongjie Cui et al.
Summary: The study developed a data-driven model integrating SSA and LightGBM for accurate and real-time prediction of urban rainfall-runoff. Evaluation results showed that the SSA-LightGBM model outperformed LightGBM and LSTM models under different conditions, with significantly shorter computation time.
JOURNAL OF HYDROLOGY
(2021)
Article
Environmental Sciences
Haibo Wang et al.
Summary: By combining satellite SIF measurements and flux tower GPP data, this study identified sensitive parameters for SIF and GPP estimation, improving the performance of the SCOPE model in simulating SIF and GPP for temperate forests. The analysis highlighted the weak capability of SIF in constraining V-cmax, while GPP was effective in constraining this parameter. The use of both satellite SIF and flux tower GPP data as constraints enhanced the model's performance in simulating both SIF and GPP simultaneously.
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Environmental Sciences
Yu Bai et al.
Summary: Global estimates of monthly gross primary production (GPP) from 2001 to 2017 were calculated using the global OCO-2-based SIF product (GOSIF) and auxiliary data. A machine learning model was used to integrate and calibrate satellite GPP products, resulting in highly accurate GPP estimates with validated spatial and seasonal variations on a global scale.
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Agronomy
Xiaojuan Huang et al.
Summary: The study optimized the parameters of the MODIS PSN model through Bayesian inference and Markov chain Monte Carlo method using FLUXNET data, aiming to quantify and reduce parameter uncertainty and improve the global MODIS GPP product for better understanding of global ecosystem carbon dynamics and plant productivity.
AGRICULTURAL AND FOREST METEOROLOGY
(2021)
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Environmental Sciences
Muhammad Sarfraz Khan et al.
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Green & Sustainable Science & Technology
Fei Chen et al.
Summary: The study investigated the dynamics of ELUE and its controlling factors in different agroecosystems. The results showed significant differences in ELUE among different crops, which were closely related to meteorological factors and water availability, providing important implications for future ecosystem simulations.
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Environmental Sciences
W. Wu et al.
Summary: A new reflectance index R(chl) was proposed to explain chlorophyll dynamics and used in the development of a simple, fast GPP model independent of climatic parameters, showing satisfactory performance in validation with field measurements.
ENVIRONMENTAL RESEARCH LETTERS
(2021)
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Agronomy
Dong Wang et al.
Summary: A three-year manipulative fire experiment in a meadow grassland on the Tibetan Plateau showed that fire increased GPP and ER, leading to an increase in NEE. Changes in plant functional type biomass post-fire may outweigh the negative effects of reduced soil moisture on ecosystem CO2 exchange.
AGRICULTURAL AND FOREST METEOROLOGY
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Environmental Sciences
Matteo Zampieri et al.
Summary: The study found that in sustainability and middle scenarios, the areas where vegetation shows increasing GPP resilience are wider than those with decreasing resilience, but this situation drastically reverses in the fossil-fuel development scenario, with Brazil being one of the highest risk countries in terms of anomalously low GPP in the taking the highway scenario.
ENVIRONMENTAL RESEARCH LETTERS
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Daning Cheng et al.
Summary: This paper investigates the scalability of parallel training algorithms in machine learning, finding that sample differences in the dataset play a significant role. The statistical properties of training datasets determine the scalability upper bound of parallel training algorithms, such as asynchronous parallel SGD, parallel model average SGD, decentralized optimization, and dual coordinate optimization.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
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Environmental Sciences
Tao Yu et al.
Summary: This study successfully upscaled ground eddy covariance systems' gross primary production (GPP) to a regional scale using machine learning methods, with random forest achieving the highest accuracy in the validation process.
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