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Due to the serious problems with energy efficiency, carbon emissions, and thermal comfort of rural residences in northern China, an optimization of active and passive heating technologies for rural residences is necessary. In this paper, an optimization for rural residences in northern China is conducted with four objectives: the whole life cycle carbon emission; the annual energy consumption through heating, ventilation, and air conditioning systems; the annual cost; and thermal comfort. In addition, the optimization model with active–passive heating technology synergy is resolved by NSGA-II genetic algorithm. The active and passive design variables, including the type of air source heat pump, orientation, the type and thickness of envelope insulation, the layer of window glass, the window-to-wall area ratio, as well as sunspace parameters are preferred to obtain the optimal solution. The results indicate that the optimal solution obtained by the ideal point method gives the most outstanding performance. Compared with the prototype, the optimized carbon emissions in severe cold and cold regions decreased by 56.1% and 54.6%, respectively. The annual energy consumption decreased by 59.7% and 62.2%. Finally, the roof insulation thickness is the most sensitive design variable in Pareto-optimal solution sets. This paper offers significant guidance in the application of the optimization method of active–passive technology synergy to the energy-saving design of buildings. [ABSTRACT FROM AUTHOR]

Land cover and its changes over time are significant for better understanding the Earth's fundamental characteristics and processes, such as global climate change, hydrology, and the carbon cycle. A number of land cover-geoprocessing models have been proposed for land cover-data production with different spatial and temporal resolutions. With the massive growth in land cover data and the increasing demand for efficient model utilization, developing efficient and convenient land cover-geoprocessing models has become a formidable challenge. Although some model-migration methods have been proposed for handling the massive data, the intricacy of land cover-data and -heterogeneity models frequently prevent current strategies from directly meeting demand. In this paper, we propose the PAMC-LC-containerization approach to overcome the difficulties associated with moving existing land cover models in the open web environment. Based on the idea of model migration, we design a standardized model description and hierarchical encapsulation strategy for land cover models, and develop migration and deployment methods. Furthermore, we assess the viability and efficacy of the proposed approach by using coupled workflows for model migration and the introduction of visualization on the Mts-WH dataset and the Google dataset. The experimental results show that the PAMC-LC approach can simplify and streamline the model migration process, with important ramifications for increasing productivity, reusing models, and lowering additional data-transmission costs. [ABSTRACT FROM AUTHOR]

This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining machine learning, remote sensing, and proximal sensing for SOC estimation and prediction across a range of applications, including comprehensive soil health mapping and carbon credit assessment. These advanced technologies offer a promising pathway, reducing costs and resource utilization while improving the precision of SOC estimation. We conducted a comparative analysis between MIR-predicted SOC values and laboratory-measured SOC values using 36 soil samples. The results demonstrate a strong fit (R² = 0.83), underscoring the potential of this integrated approach. While acknowledging that our analysis is based on a limited sample size, these initial findings offer promise and serve as a foundation for future research. We will be providing updates when we obtain more data. Furthermore, this paper explores the potential for commercialising these technologies in Australia, with the aim of helping farmers harness the advantages of carbon markets. Based on our study's findings, coupled with insights from the existing literature, we suggest that adopting this integrated SOC measurement approach could significantly benefit local economies, enhance farmers' ability to monitor changes in soil health, and promote sustainable agricultural practices. These outcomes align with global climate change mitigation efforts. Furthermore, our study's approach, supported by other research, offers a potential template for regions worldwide seeking similar solutions. [ABSTRACT FROM AUTHOR]

Simple Summary: In this paper, the differences in carbon sequestration microbial communities in different wetland types and their main influencing factors were investigated. It was found that the alpha diversity of cbbM carbon-sequestering microorganisms was consistent with the change trend in the total carbon content. Acidithiobacillus was used as a biomarker in lakeside wetlands, and Thiothrix and Thiodictyon were used as biomarkers in marsh wetlands. The diversity of cbbM carbon-fixing microorganisms was primarily influenced by the total nitrogen content, while the community structure was significantly affected by the soil total carbon content. The increase in soil temperature and humidity was conducive to the carbon-sequestering process of Thiomicrospira, Thiomonas, Polaromonas and Acidithiobacillus. The changes in wetland types seriously affected the characteristics of cbbM carbon sequestration in microbial communities, and a warm and humid climate may be conducive to wetland carbon sequestration. Carbon-sequestering microorganisms play an important role in the carbon cycle of wetland ecosystems. However, the response mechanism of carbon-sequestering microbial communities to wetland type changes and their relationship with soil carbon remain unclear. To explore these differences and identify the main influencing factors, this study selected marsh wetlands, river wetlands and lakeside wetlands around Qinghai Lake as research subjects. High-throughput sequencing was employed to analyze the functional gene cbbM of carbon-sequestering microorganisms. The results revealed that the alpha diversity of cbbM carbon-sequestering microorganisms mirrored the trend in total carbon content, with the highest diversity observed in marsh wetlands and the lowest in lakeside wetlands. The dominant bacterial phylum was Proteobacteria, with prevalent genera including Thiothrix, Acidithiobacillus, and Thiodictyon. Acidithiobacillus served as a biomarker in lakeside wetlands, while two other genera were indicative of marsh wetlands. The hierarchical partitioning analysis indicated that the diversity of cbbM carbon-fixing microorganisms was primarily influenced by the total nitrogen content, while the community structure was significantly affected by the soil total carbon content. Moreover, an increased soil temperature and humidity were found to favor the carbon fixation processes of Thiomicrospira, Thiomonas, Polaromonas, and Acidithiobacillus. In summary, changes in wetland types seriously affected the characteristics of cbbM carbon sequestration in microbial communities, and a warm and humid climate may be conducive to wetland carbon sequestration. [ABSTRACT FROM AUTHOR]

Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R2 of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R2 of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R2 of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation. [ABSTRACT FROM AUTHOR]

Forest stock volume is the main factor to evaluate forest carbon sink level. At present, the combination of multi-source remote sensing and non-parametric models has been widely used in FSV estimation. However, the biodiversity of natural forests is complex, and the response of the spatial information of remote sensing images to FSV is significantly reduced, which seriously affects the accuracy of FSV estimation. To address this challenge, this paper takes China's Baishanzu Forest Park with representative characteristics of natural forests as the research object, integrates the forest survey data, SRTM data, and Landsat 8 images of Baishanzu Forest Park, constructs a time series dataset based on survey time, and establishes an FSV estimation model based on the CNN-LSTM-Attention algorithm. The model uses the convolutional neural network to extract the spatial features of remote sensing images, uses the LSTM to capture the time-varying characteristics of FSV, captures the feature variables with a high response to FSV through the attention mechanism, and finally completes the prediction of FSV. The experimental results show that some features (e.g., texture, elevation, etc.) of the dataset based on multi-source data feature variables are more effective in FSV estimation than spectral features. Compared with the existing models such as MLR and RF, the proposed model achieved higher accuracy in the study area (R2 = 0.8463, rMSE = 26.73 m3/ha, MAE = 16.47 m3/ha). [ABSTRACT FROM AUTHOR]

Simple Summary: In this paper, the differences in carbon sequestration microbial communities in different wetland types and their main influencing factors are investigated. It was found that Proteobacteria was the dominant group of carbon sequestration microorganisms in the marsh wetland and river source wetland, and Cyanobacteria was the dominant group of carbon sequestration microorganisms in the lakeside wetland. The α diversity, relative abundance of Proteobacteria and total carbon content of the marsh wetland were the highest, followed by the river source wetland and lakeside wetland. The soil temperature and moisture and the total carbon content were the most important factors affecting the structures of carbon-sequestering microbial communities. Compared with the riverhead and lakeside wetlands, the marsh wetland has the highest carbon storage. The soil carbon storage in the Qinghai–Tibet Plateau wetlands is affected by microbiota and wetland types, but the response mechanisms of carbon sequestration microorganisms on the Qinghai–Tibet Plateau to different wetland types are still poorly described. To explore the differences in carbon sequestration microbial communities in different wetlands and the main influencing factors, this study took a marsh wetland, river source wetland and lakeside wetland of Qinghai Lake as the research objects and used high-throughput sequencing to study the functional gene, cbbL, of carbon sequestration microorganisms. The results showed that the dominant bacterial group of carbon sequestration microorganisms in marsh and river source wetlands was Proteobacteria, and the dominant bacterial group in the lakeside wetland was Cyanobacteria. The alpha diversity, relative abundance of Proteobacteria and total carbon content were the highest in the marsh wetland, followed by the river source wetland, and they were the lowest in the lakeside wetland. In addition, the physical and chemical characteristics of the three wetland types were significantly different, and the soil temperature and moisture and total carbon content were the most important factors affecting the community structures of carbon-sequestering microorganisms. There was little difference in the total nitrogen contents between the marsh wetland and river source wetland. However, the total nitrogen content was also an important factor affecting the diversity of the carbon sequestration microbial community. In summary, the wetland type significantly affects the process of soil carbon sequestration. Compared with the riverhead and lakeside wetlands, the marsh wetland has the highest carbon storage. [ABSTRACT FROM AUTHOR]

This paper discusses and reviews the main real gas effects on the thermodynamic performance of closed Brayton cycles. Cycles with carbon dioxide as working fluids are taken as a reference and a comparison of the thermodynamic cycle efficiencies that are made with other possible working fluids (pure fluids and fluid mixtures). We fixed the reduced operating conditions, in optimal conditions, so that all working fluids had the same thermodynamic global performances. Therefore, the choice of the working fluid becomes important for adapting the cycle to the different technological requirements. The positive effects of the real gas properties in supercritical cycles were approximately maximal at reduced minimum cycle temperatures of about 1.01 to 1.05, with maximum to minimum cycle temperatures of about 2.2. The use of mixtures furthers widens the application of the field of closed Brayton cycles, thereby allowing a continuous variation in the critical temperature of the resulting working fluid and, in some cases, also making it possible to take the condensation with a significant further increase in the thermodynamic cycle efficiency. The paper also demonstrates the thermodynamic convenience of resorting to mixtures of carbon dioxide and inert gases. Extensive measurements of vapour–liquid equilibria and analysis of the thermal stability and material compatibility are essential for a practical and full use of the real gas Brayton cycles. [ABSTRACT FROM AUTHOR]

Under the background of double carbon, it is important to study forest pests and diseases to improve forest carbon sink. In this paper, we establish a delayed model associated with the larvae and adults of Saperda populnea, susceptible poplars, and infected poplars. First, we analyze the existence and stability of the equilibrium of the model. Second, we study the existence of Hopf bifurcation near the equilibrium and obtain the normal form of Hopf bifurcation by the multiple time scales method. Then, we analyze the direction and stability of Hopf bifurcating periodic solutions. Third, we analyze and conjecture some parameter values based on official data, and carry out numerical simulations to verify our results. Finally, we give some suggestions on the prevention and control of Saperda populnea. [ABSTRACT FROM AUTHOR]

The organic carbon pool is a crucial component of the ocean carbon cycle. The study of organic carbon distribution and interannual variability in the land-sea interface can contribute to understanding the global ocean carbon cycle and ecological effects in the context of the Anthropocene and help achieve the Sustainable Development Goals (SDGs). At present, there has been a certain amount of research on the source and flux of carbon in the ocean carbon cycle, but the prediction of marine carbon is still in its infancy. In this paper, a CNN-LSTM deep learning model that takes into account spatio-temporal features was used to make a 5-year mid-long-term rolling prediction of particulate organic carbon (POC) and yellow matter (CDOM) using MODIS Level 2 semimonthly synthetic data from the official website of NASA from January 2002 to June 2020. The model uses chlorophyll-a data to adjust the parameters. The results showed that the model could also be applied to the mid-long-term rolling prediction of POC and CDOM. The model was capable of accurately predicting POC and CDOM over periods of three and two years, respectively (R > 0.5). Meanwhile, the 5-year trends of the predicted and actual values were verified using the least squares method and the Mann-Kendall trend test. The results showed that the predicted and actual values of sea surface POC and CDOM in 2015–2020 showed an overall upward trend. The surface-level POC and CDOM in the ocean are considered to be related to primary production. The mid-long-term prediction of surface seawater organic carbon in the southern South China Sea helps humans explore the regional characteristics of organic carbon in the coral reef waters of the South China Sea and study the changing trend of surface seawater organic carbon. [ABSTRACT FROM AUTHOR]

The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research. [ABSTRACT FROM AUTHOR]

Forests cover approximately one-third of the Earth's land surface and constitute the core region of the carbon cycle on Earth. The paramount importance and multi-purpose applications of forest monitoring have gained widespread recognition over recent decades. Polarimetric synthetic aperture radar interferometry (PolInSAR) has been demonstrated as a promising technique to retrieve the forest height over large areas with a limited cost. This paper presents an overview of forest height inversion (FHI) techniques based on PolInSAR data. Firstly, we introduce the basic theories of PolInSAR and FHI procedures. Next, we review the established data-based algorithms for single-baseline data and describe innovative techniques related to multi-baseline data. Then, the model-based algorithms are also introduced with their corresponding forest scattering models under multiple data acquisition modes. Subsequently, a case study is presented to demonstrate the applicable scenarios and advantages of different algorithms. Model-based algorithms can provide accurate results when the scene and forest properties are well understood and the model assumptions are valid. Data-based algorithms, on the other hand, can handle complex scattering scenarios and are generally more robust to uncertainties in the input parameters. Finally, the prospect of forest height inversion was analyzed. It is our hope that this review will provide guidelines to future researchers to enhance further FHI algorithmic developments. [ABSTRACT FROM AUTHOR]

China's largest high-latitude permafrost distribution zone is in Northeast China. With the intensification of global warming and engineering construction, the carbon stored in permafrost will gradually thaw and be released in the form of methane gas. However, research on the changes in methane concentration and emission sources in this area is still unclear. In this paper, the AIRS (Atmospheric Infrared Sounder) data carried by the Aqua satellite were used to analyze the distribution and change trends in the overall methane concentration in the near-surface troposphere in Northeast China from 2003 to 2022. These data, combined with national meteorological and on-site monitoring data, were used to study the methane emission characteristics and sources in the permafrost area in Northeast China. The results show that the methane concentration in the near-surface troposphere of Northeast China is mainly concentrated in the permafrost area of the Da and Xiao Xing'an Mountains. From 2003 to 2022, the methane concentration in the near-surface troposphere of the permafrost area in Northeast China showed a rapid growth trend, with an average linear trend growth rate of 4.787 ppbv/a. In addition, the methane concentration in the near-surface troposphere of the permafrost area shows a significant bimodal seasonal variation pattern. The first peak appears in summer (June–August), with its maximum value appearing in August, and the second peak appears in winter (December–February), with its maximum value appearing in December. Combined with ground surface methane concentration monitoring, it was found that the maximum annual ground surface methane concentration in degraded permafrost areas occurred in spring, causing the maximum average growth rate in methane concentration, also in spring, in the near-surface troposphere of permafrost areas in Northeast China (with an average value of 6.05 ppbv/a). The growth rate of methane concentration in the southern permafrost degradation zone is higher than that in the northern permafrost stable zone. In addition, with the degradation of permafrost, the geological methane stored deep underground (methane hydrate, coal seam, etc., mainly derived from the accumulation of ancient microbial origin) in the frozen layer will become an important source of near-surface troposphere methane in the permafrost degradation area. Due to the influence of high-permeability channels after permafrost degradation, the release rate of methane gas in spring is faster than predicted, and the growth rate of methane concentration in the near-surface troposphere of permafrost areas can be increased by more than twice. These conclusions can provide a data supplement for the study of the carbon cycle in permafrost areas in Northeast China. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) change has greatly altered ecosystem carbon storage and exerted an enormous impact on terrestrial carbon cycling. Characterizing its impact on ecosystem carbon storage is critical to balance regional carbon budgets and make land use decisions. However, due to the availability of LULC data and the strong variability in LULC change, uncertainty remains high in quantifying the effect of LULC change on the historical and future carbon stock. Based on four historical LULC maps and one future LULC projection, this study combined the Land Use and Carbon Scenario Simulator (LUCAS) with a process-based CENTURY model to evaluate the historical and future LULC change and its impact on grassland carbon storage from 1991 to 2050 in northern China. Results showed that grassland experienced a drastic decrease of 16.10 × 103 km2 before 2005, while agriculture and barren land increased by 16.91 × 103 km2 and 3.73 × 103 km2, respectively. After that, grassland was projected to increase, agriculture kept steady, and barren land decreased. LULC change has resulted in enormous total ecosystem carbon loss, mainly in agro-pasture areas; the maximum 8.54% of carbon loss happened in 2000, which was primarily attributed to agriculture to grassland, forest to grassland, grassland to agriculture, and grassland to barren. Before 2000, the grassland net biome productivity was projected to be −15.54 Tg C/yr and −2.69 Tg C/yr with and without LULC change. After 2001, the LULC change showed a positive impact on the grassland carbon balance, and the region was projected to be a carbon sink. Ecological projects have made a significant contribution to grassland carbon storage. The paper provides a framework to account for the effects of LULC change on ecosystem carbon and highlights the importance of improving grassland management in balancing the grassland carbon budget, which is helpful to understand the regional carbon budget and better inform local land use strategies. [ABSTRACT FROM AUTHOR]

Grassland gross primary productivity (GPP) is an important part of global terrestrial carbon flux, and its accurate simulation and future prediction play an important role in understanding the ecosystem carbon cycle. Machine learning has potential in large-scale GPP prediction, but its application accuracy and impact factors still need further research. This paper takes the Mongolian Plateau as the research area. Six machine learning methods (multilayer perception, random forest, Adaboost, gradient boosting decision tree, XGBoost, LightGBM) were trained using remote sensing data (MODIS GPP) and 14 impact factor data and carried out the prediction of grassland GPP. Then, using flux observation data (positions of flux stations) and remote sensing data (positions of non-flux stations) as reference data, detailed accuracy evaluation and comprehensive trade-offs are carried out on the results, and key factors affecting prediction performance are further explored. The results show that: (1) The prediction results of the six methods are highly consistent with the change tendency of the reference data, demonstrating the applicability of machine learning in GPP prediction. (2) LightGBM has the best overall performance, with small absolute error (mean absolute error less than 1.3), low degree of deviation (root mean square error less than 3.2), strong model reliability (relative percentage difference more than 5.9), and a high degree of fit with reference data (regression determination coefficient more than 0.97), and the prediction results are closest to the reference data (mean bias is only −0.034). (3) Enhanced vegetation index, normalized difference vegetation index, precipitation, land use/land cover, maximum air temperature, potential evapotranspiration, and evapotranspiration are significantly higher than other factors as determining factors, and the total contribution ratio to the prediction accuracy exceeds 95%. They are the main factors influencing GPP prediction. This study can provide a reference for the application of machine learning in GPP prediction and also support the research of large-scale GPP prediction. [ABSTRACT FROM AUTHOR]

Clarifying the pattern of the urban greenhouse gas (GHG) budget and its influencing factors is the basis of promoting urban low-carbon development. This paper takes Beijing and Shenzhen—the capital city and the most rapidly developing city in China, respectively—as case studies, comprehensively accounts their GHG budgets from 2005 to 2020, and investigates and compares the factors affecting their GHG budgets. The total GHG emissions in Beijing were lowest in 2005 (160.3 TgCO2 equivalents) and peaked at 227.7 TgCO2 equivalents in 2019, and then decreased to 209.1 TgCO2 equivalents in 2020. Meanwhile, the total GHG emissions in Shenzhen gradually increased from 36.0 TgCO2 equivalents in 2005 to 121.4 TgCO2 equivalents in 2019, and then decreased to 119.1 TgCO2 equivalents in 2020. The energy activity sector was the greatest contributor to GHG emissions in this period, accounting for 82.5% and 76.0% of the total GHG emissions in Beijing and Shenzhen, respectively. The carbon sink of the ecosystems of these two cities could absorb only small parts of their emissions, and the neutralization rates of sinks ranged from 1.7% to 2.3% in Beijing and from 0.3% to 1.5% in Shenzhen. The enhancement of population, economic product, and consumption increased the greenhouse gas emissions in both cities. A 1% increase in population size, per capita GD (gross domestic product), and residential consumption level would increase total GHG emissions by 0.181%, 0.019%, and 0.030% in Beijing, respectively. The corresponding increases in Shenzhen would be 0.180%, 0.243%, and 0.172%, respectively. The household size had opposite effects on the two cities, i.e., a 1% increase in household size would increase GHG emissions by 0.487% in Shenzhen but reduce them by 2.083% in Beijing. Each 1% increase in secondary industry and energy intensity would reduce GHG emissions by 0.553% and 0.110% in Shenzhen, respectively, which are more significant reductions than those in Beijing. [ABSTRACT FROM AUTHOR]

The soils of cold regions store up to 60% of all organic carbon on the planet. As a result of climate change, this organic matter can be biodegraded by microorganisms and thereby make an additional contribution to carbon balance. Nowadays, there are fragmentary data on organic C stocks in high-latitude soils and single works on the analysis of the quality of buried organic matter. This paper presents the first data on the molecular weight distributions of humic acids (HAs) extracted from soils and sediments in Yedoma. Molecular weight distributions of HAs preparations were obtained on an AKTAbasic 10 UPS chromatographic system (Amersham Biosciences, Sweden) using a SuperdexTM 200 10/300 GL column (with cross-linked dextran gel, fractionation range for globular proteins 10–600 kDa). As a result of the study, it was found that the buried soil horizons are characterized by the highest content of low molecular weight fraction (with molecular mass (Mr) 1.4–1.9 kDa and molar fraction in the range of 54.3–67.1%). The high molecular weight fraction is concentrated mainly in the superficial horizons and decreases with depth (with Mr 299–346 kDa and molar fraction in the range of 3.4–9.8%). In the Yedoma sediments, the maximum content of the medium-molecular fraction is observed (with Mr 24.6 kDa and 39.6% of the molar fraction), which may indicate a low rate of organic matter transformation in the permafrost. The data obtained serve as a database of analysis in terms of modeling the global carbon cycle in the cold regions of the planet. [ABSTRACT FROM AUTHOR]

Land-use–cover change (LUCC)/vegetation cover plays a critical role in Earth system science and is a reflection of human activities and environmental changes. LUCC will affect the structure and function of ecosystems and a series of other terrestrial surface processes, such as energy exchange, water circulation, biogeochemical circulation, and vegetation productivity. Therefore, accurate LUCC mapping and vegetation cover monitoring are the bases for simulating the global carbon and hydrological cycles, studying the interactions of the land surface and climate, and assessing land degradation. Based on field GPS surveys and UAV data, with cloud-free and snow/glacier algorithms and the SVM classifier to train and model alpine grassland, the alpine grassland and LUCC were extracted by using Landsat-8 OLI satellite images in Sanjiangyuan National Park in this paper. The latest datasets of vegetation types with 30 m × 30 m spatial resolution in the three parks were prepared and formed. The classification results show that the SVM classifier could better distinguish the major land-use types, and the overall classification accuracy was very high. However, in the alpine grassland subcategories, the classification accuracies of the four typical grasslands were relatively low, especially between desert steppes and alpine meadows, and desert steppes and alpine steppes. It manifests the limitations of Landsat-8 multispectral remote sensing imageries in finer-resolution grassland classifications of high-altitude alpine mountains. The method can be utilized for other multispectral satellite imageries with the same band matching, such as Landsat 7, Landsat 9, Sentinel-2, etc. The method described in this paper can rapidly and efficiently process annual alpine grassland maps of the source areas of the Yellow River, the Yangtze River, and the Lancang River. It can provide timely and high-spatial-resolution datasets for supporting scientific decisions for the sustainable management of Sanjiangyuan National Park. [ABSTRACT FROM AUTHOR]

Forests are critical to mitigating global climate change and regulating climate through their role in the global carbon and water cycles. Accurate monitoring of forest cover is, therefore, essential. Image segmentation networks based on convolutional neural networks have shown significant advantages in remote sensing image analysis with the development of deep learning. However, deep learning networks typically require a large amount of manual ground truth labels for training, and existing widely used image segmentation networks struggle to extract details from large-scale high resolution satellite imagery. Improving the accuracy of forest image segmentation remains a challenge. To reduce the cost of manual labelling, this paper proposed a data augmentation method that expands the training data by modifying the spatial distribution of forest remote sensing images. In addition, to improve the ability of the network to extract multi-scale detailed features and the feature information from the NIR band of satellite images, we proposed a high-resolution forest remote sensing image segmentation network by fusing multi-scale features based on double input. The experimental results using the Sanjiangyuan plateau forest dataset show that our method achieves an IoU of 90.19%, which outperforms prevalent image segmentation networks. These results demonstrate that the proposed approaches can extract forests from remote sensing images more effectively and accurately. [ABSTRACT FROM AUTHOR]

Urbanization profoundly impacts the global carbon cycle and climate change. Many studies have shown that both urban vitality and urban carbon emissions are deeply affected by spatial planning and city structure. However, the specific relationship between urban vitality and urban carbon emissions is rarely studied. An index system of urban vitality was established from four aspects: social, economic, cultural, and environmental. After analyzing the spatial distribution characteristics of urban vitality combined with spatial syntax and the TOPSIS model, this paper further investigated the influence of urban vitality-building factors on the distribution of urban carbon emissions based on the Geodetector method. The research results show that: (1) Xuzhou shows obvious spatial differences in urban vitality, mainly decreasing from the center to the surrounding areas, with a small vitality center in the northeast. (2) The impact of different dimensions of vitality on urban carbon emissions is apparently different. (3) Facilities' aggregation has the weakest explanatory power for urban carbon emissions, while the NDVI has the highest explanatory power. This study helps to clarify the spatial correlation and influence mechanism between urban vitality and urban carbon emissions. Finally, some suggestions are proposed to construct low-carbon and high-vitality cities. [ABSTRACT FROM AUTHOR]

In this paper, recent research on terrestrial ecosystem predictability using the conditional nonlinear optimal parameter perturbation (CNOP-P) method is summarized. The main findings include the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, the identification of key physical parameters that lead to large uncertainties in terrestrial ecosystem modeling and prediction, and the evaluation of the simulation ability and prediction skill of terrestrial ecosystems by reducing key physical parameter errors. The study areas included the Inner Mongolia region, north–south transect of eastern China, and Qinghai–Tibet Plateau region. The periods of the studies were from 1961 to 1970 for the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, and from 1951 to 2000 for the identification of the most sensitive combinations of physical parameters. Climatic Research Unit (CRU) data were employed. The numerical results indicate the important role of nonlinear changes in climate variability due to the occurrences of extreme events characterized by CNOP-P in the abrupt grassland ecosystem equilibrium state and formation of carbon sinks in China. Second, the most sensitive combinations of physical parameters to the uncertainties in simulations and predictions of terrestrial ecosystems identified by the CNOP-P method were more sensitive than those obtained by traditional methods (e.g., one-at-a-time (OAT) and stochastic methods). Furthermore, the improvement extent of the simulation ability and prediction skill of terrestrial ecosystems by reducing the errors of the sensitive physical parameter combinations identified by the CNOP-P method was higher than that by the traditional methods. [ABSTRACT FROM AUTHOR]

In this paper, the mechanism of separation of volatile organic compounds (VOCs) from activated carbon adsorption beds during closed cycle temperature swing desorption was studied. Toluene gas at different concentrations was used as the gas for closed cycle temperature swing desorption to regenerate activated carbon beds saturated with toluene. This research advances our understanding of the separation of contaminants from activated carbon and the mechanism of the process by which waste gas with a background concentration desorbs activated carbon in hot gas with a background concentration, establishing a technological foundation for the closed cycle temperature swing desorption process of activated carbon. When the background concentration was 2 g/m3, the average desorption rates of unit activated carbon at 10 cm in 40 min and 60 min were the largest, at 0.0099 and 0.0067 g/ (g•min), respectively. The fit of the Bangham desorption rate equation was the best. When the background concentration of toluene was 2 g/m3 and the filling length of the activated carbon layer was 10 cm, the desorption rate constant was the highest, at 0.0152 min−1. [ABSTRACT FROM AUTHOR]

Soil inorganic carbon (SIC) is an essential component of the soil carbon pool and plays a vital role in the global carbon cycle and climate change. However, few studies have examined the effects of different agronomic practices on the SIC content. This paper aims to study the effects of different agronomic practices on the SIC content in the 0- to 40-cm soil layer of dryland fields; the innovations are intended to explore the relative importance and synergistic effects of different agronomic practices on the SIC content of the plough layer of dryland fields. We screened out 136 peer-reviewed articles worldwide from 1990–2022, with a total of 2612 valid data pairs, using meta-analysis to assess the effects of different agronomic practices on the SIC content of the plough layer of dryland fields. Compared to conventional tillage (CT), both no-tillage (NT) and plastic film mulching (PM) were able to increase the SIC content in the 0- to 40-cm soil layer of dryland fields. NT increased the SIC content by 15.07 ± 3.48%, while PM gradually increased SIC accumulation as the soil layer deepened, with the greatest increase in SIC content in the 30- to 40-cm soil layer at 11.61 ± 5.89%. When organic manure application (M) and straw return mulching (SM) were applied, the SIC content in the 0- to 40-cm soil layer of dryland fields showed a non-significant reduction trend, with the largest changes in SIC content in the 0- to 10-cm soil layer, at 5.23 ± 2% and 4.69 ± 3.53% reductions, respectively. No-tillage straw return (NTS) significantly increased the SIC content in the 0- to 40-cm soil layer of dryland fields by 77.34 ± 5.6%, which was significantly higher than the independent effects of NT and SM, showing a more substantial synergistic effect. Different agronomic practices have different effects on the SIC content of dryland fields in the 0- to 40-cm soil layer, with NTS > NT > PM > SM > M. Among practices, NTS, NT and PM can increase the SIC content of the plough layer of dryland fields; in particular, NTS can increase the SIC content of 0- to 40-cm in dryland fields to the greatest extent through a synergistic effect. At the same time, SM and M showed an insignificant reduction effect. [ABSTRACT FROM AUTHOR]

Forests are the main part of the terrestrial ecosystem. Airborne LiDAR is fast, comprehensive, penetrating, and contactless and can depict 3D canopy information with a high efficiency and accuracy. Therefore, it plays an important role in forest ecological protection, tree species recognition, carbon sink calculation, etc. Accurate recognition of individual trees in forests is a key step to various application. In real practice, however, the accuracy of individual tree segmentation (ITS) is often compromised by under-segmentation due to the diverse species, obstruction and understory trees typical of a high-density multistoried mixed forest area. Therefore, this paper proposes an ITS optimization method based on Gaussian mixture model for airborne LiDAR data. First, the mean shift (MS) algorithm is used for the initial ITS of the pre-processed airborne LiDAR data. Next, under-segmented samples are extracted by integrated learning, normally segmented samples are classified by morphological approximation, and the approximate distribution uncertainty of the normal samples is described with a covariance matrix. Finally, the class composition among the under-segmented samples is determined, and the under-segmented samples are re-segmented using Gaussian mixture model (GMM) clustering, in light of the optimal covariance matrix of the corresponding categories. Experiments with two datasets, Trento and Qingdao, resulted in ITS recall of 94% and 96%, accuracy of 82% and 91%, and F-scores of 0.87 and 0.93. Compared with the MS algorithm, our method is more accurate and less likely to under-segment individual trees in many cases. It can provide data support for the management and conservation of high-density multistoried mixed forest areas. [ABSTRACT FROM AUTHOR]

With regards to oil-based diesel fuel, the adoption of bio-derived diesel fuel was estimated to reduce CO2 emissions by approximately 75%, considering the whole life cycle. In this paper, we present a novel continuous-flow process able to transfer an equimolar amount of CO2 (through urea) to glycerol, producing glycerol carbonate. This represents a convenient tool, able to both improve the efficiency of the biodiesel production through the conversion of waste streams into added-value chemicals and to beneficially contribute to the whole carbon cycle. By means of a Design of Experiments approach, the influence of key operating variables on the product yield was studied and statistically modeled. [ABSTRACT FROM AUTHOR]

It was time to take stock. We modified the humipedon classification key published in 2018 to make it easier and more practical. This morpho-functional taxonomy of the topsoil (humipedon) was only available in English; we also translated it into French and Italian. A standardized morpho-functional classification of humipedons (roughly the top 30–40 cm of soil: organic and organomineral surface horizons) would allow for a better understanding of the functioning of the soil ecosystem. This paper provides the founding principles of the classification of humipedon into humus systems and forms. With the recognition of a few diagnostic horizons, all humus systems can be determined. The humus forms that make up these humus systems are revealed by measuring the thicknesses of the diagnostic horizons. In the final part of the article, several figures represent the screenshots of a mobile phone or tablet application that allows for a fast recall of the diagnostic elements of the classification in the field. The article attempts to promote a standardized classification of humipedons for a global and shared management of soil at planet level. [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series provide a 50-year data archive, which is a valuable source for historical mapping over large areas. As such, this paper proposed a machine learning-based workflow for historical AGB estimation and its change analysis from 2001 to 2019 for the New York State's forests using Landsat historical imagery, airborne LiDAR, and forest plot data. As the object-based image analysis (OBIA) is able to incorporate spectral, contextual, and textural features into the regression model, the proposed method utilizes an OBIA approach and a random forest (RF) regression model implemented on the Google Earth Engine (GEE) cloud computing platform. Results demonstrated that there is a considerable decrease of 983.79 × 106 Mg/ha in the AGB of deciduous forests from 2001 to 2006, followed by an increase of 618.28 × 106 Mg/ha from 2006 to 2011, continued with an increase of 229.12 × 106 Mg/ha of deciduous forests from 2011–2016. Finally, the results demonstrated a slight change in AGB from 2016 to 2019. The transferability of the proposed framework provides a practical solution for monitoring forests in other states or even on a national scale. [ABSTRACT FROM AUTHOR]

The distribution pattern and flux variation of POC in the continental shelf seas are essential for understanding the carbon cycle in marginal seas. The hydrodynamic environment and complicated estuarine processes in the East China Sea result in challenging estimates and substantial spatio-temporal variability in terms of POC concentrations. A hybrid retrieval model based on the mutual combination of the color index algorithm (CIPOC) and the empirical band ratio algorithm was applied in this study to effectively and dynamically monitor the surface POC concentration in the East China Sea in a long-term series for the first time using MODIS/Aqua remote sensing satellite data from 2003 to 2020. A hybrid retrieval model based on the mutual combination of the color index algorithm (CIPOC) and the empirical band ratio algorithm was applied in this study. The MODIS/Aqua remote sensing satellite data from 2003 to 2020 were employed for the first time to dynamically monitor the surface POC concentrations in the East China Sea for a long time series. The results demonstrated that the performance (R2 = 0.84, RMSE = 156.14 mg/m3, MAPE = 43.30%, bias = −64.79 mg/m3) exhibited by this hybrid retrieval algorithm confirms the usability of inversion studies of surface POC in the East China Sea. Different drivers such as river discharge, phytoplankton, wind, and the sea surface current field jointly influence the spatial and temporal distribution of POC concentrations in the East China Sea. This paper also verifies that the hybrid algorithm can be applied to retrieval tasks for POC in different seas with similar optical properties to the waters of the East China Sea. In conclusion, the long-term series East China Sea POC data record, which was established based on MODIS/Aqua, provides supplementary information for in-situ sampling, which will aid the long-term monitoring of POC fluxes in shelf seas. At the same time, it has also improved our understanding of the transport and spatio-temporal variability of POC in the East China Sea, enhancing our comprehension of the impact of POC on environmental changes and carbon cycling in marginal seas. [ABSTRACT FROM AUTHOR]

The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite. [ABSTRACT FROM AUTHOR]

Simple Summary: Green snow microorganisms play an important role in biogeochemical cycle and carbon sink processes and they can be a source of biotechnologically interesting cell factories. A wide temperature tolerance is a unique property of bacteria isolated from cold environments, which has received great attention in the last years. The present paper examines the growth and chemical profile flexibility for green snow bacteria exposed to different temperature and nutrient fluctuations. By applying high-throughput chemical phenotyping with FTIR spectroscopy we discovered chemical changes possessed by green snow bacteria when grown at high/low temperature and rich/minimal media. Temperature fluctuations and nutrient composition are the main parameters influencing green snow microbiome. In this study we investigated the influence of temperature and nutrient conditions on the growth and cellular chemical profile of bacteria isolated from green snow. Chemical profiling of the green snow bacteria was done by high-throughput FTIR spectroscopy combined with multivariate data analysis. We showed that temperature and nutrients fluctuations strongly affect growth ability and chemical profile of the green snow bacteria. The size of colonies for green snow bacteria grown at higher (25 °C) and lower (4 °C and 10 °C) than optimal temperature (18 °C) was smaller. All isolates grew on rich medium, and only 19 isolates were able to grow on synthetic minimal media. Lipid and mixed spectral regions showed to be phylogeny related. FTIR fingerprinting indicates that lipids are often affected by the temperature fluctuations. Growth on different media resulted in the change of the whole chemical profile, where lipids showed to be more affected than proteins and polysaccharides. Correlation analysis showed that nutrient composition is clearly strongly influencing chemical changes in the cells, followed by temperature. [ABSTRACT FROM AUTHOR]

Net ecosystem productivity (NEP) plays an important role in understanding ecosystem function and the global carbon cycle. In this paper, the key parameters of the Carnegie Ames Stanford Approach (CASA) model, maximum light use efficiency (εmax), was optimized by using vegetation classification data. Then, the NEP was estimated by coupling the optimized CASA model, geostatistical model of soil respiration (GSMSR) and the soil respiration–soil heterotrophic respiration (Rs-Rh) relationship model. The ground observations from ChinaFLUX were used to verify the NEP estimation accuracy. The results showed that the R2 of the optimized CASA model increased from 0.411 to 0.774, and RMSE decreased from 21.425 gC·m−2·month−1 to 12.045 gC·m−2·month−1, indicating that optimizing CASA model by vegetation classification data was an effective method to improve the estimation accuracy of NEP. On this basis, the spatial and temporal distribution of NEP in China was analyzed. The research indicated that the monthly variation of NEP in China was a single peak curve with summer as the peak, which generally presented the pattern of southern region > northern region > Qinghai–Tibet region > northwest region. Furthermore, from 2001 to 2016, most regions of China showed a non-significant level upward trend, but main cropland (e.g., North China Plain and Northeast Plain) and some grassland (e.g., Ngari in Qinghai–Tibet Plateau and Xilin Gol League in Inner Mongolia) showed a non-significant-level downward trend. The study can deepen the understanding of the distribution of carbon sources/sinks in China, and provide a reference for regional carbon cycle research. [ABSTRACT FROM AUTHOR]

Several global leaf area index (LAI) products were generated using neural networks, but the training dataset for the neural networks was sensor specific, and the construction of the training dataset was time consuming. In this paper, an unsupervised domain adaptation-based method was proposed to estimate LAI from the Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance dataset based on a training dataset constructed from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance dataset. A transfer component analysis (TCA) algorithm was first utilized to map the MODIS and VIIRS surface reflectance into the same subspace to reduce the distribution discrepancies between the MODIS and VIIRS surface reflectance. Then, the embedded data obtained from MODIS surface reflectance dataset, along with the LAI values produced by fusing the MODIS and the Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) products, were employed to train general regression neural networks (GRNNs). Finally, for retrieving the LAI values, the embedded data acquired from the VIIRS surface reflectance dataset was input into the trained GRNNs. For multiple field sites with different biome types, we used this developed method to retrieve LAI values based on the VIIRS surface reflectance dataset. The results indicate that, based on the training dataset built from MODIS surface reflectance dataset, the domain adaptation-based retrieval method can effectively estimate LAI values from VIIRS surface reflectance dataset. By comparison with the VIIRS and MODIS LAI products, the retrieved LAI values with TCA are more consistent with the reference LAI values acquired from high-resolution remote sensing images. The coefficient of determination (R2) and root mean square error (RMSE) of the retrieved LAI values with TCA at all selected sites are 0.88 and 0.68, respectively. Furthermore, the accuracy of the retrieved LAI values with TCA is higher than the retrieved LAI values without TCA with the R2 0.81 and the RMSE 0.79. [ABSTRACT FROM AUTHOR]

Spatially-detailed forest height data are useful to monitor local, regional and global carbon cycle. LiDAR remote sensing can measure three-dimensional forest features but generating spatially-contiguous forest height maps at a large scale (e.g., continental and global) is problematic because existing LiDAR instruments are still data-limited and expensive. This paper proposes a new approach based on an artificial neural network (ANN) for modeling of forest canopy heights over the China continent. Our model ingests spaceborne LiDAR metrics and multiple geospatial predictors including climatic variables (temperature and precipitation), forest type, tree cover percent and land surface reflectance. The spaceborne LiDAR instrument used in the study is the Geoscience Laser Altimeter System (GLAS), which can provide within-footprint forest canopy heights. The ANN was trained with pairs between spatially discrete LiDAR metrics and full gridded geo-predictors. This generates valid conjugations to predict heights over the China continent. The ANN modeled heights were evaluated with three different reference data. First, field measured tree heights from three experiment sites were used to validate the ANN model predictions. The observed tree heights at the site-scale agreed well with the modeled forest heights (R = 0.827, and RMSE = 4.15 m). Second, spatially discrete GLAS observations and a continuous map from the interpolation of GLAS-derived tree heights were separately used to evaluate the ANN model. We obtained R of 0.725 and RMSE of 7.86 m and R of 0.759 and RMSE of 8.85 m, respectively. Further, inter-comparisons were also performed with two existing forest height maps. Our model granted a moderate agreement with the existing satellite-based forest height maps (R = 0.738, and RMSE = 7.65 m (R² = 0.52, and RMSE = 8.99 m). Our results showed that the ANN model developed in this paper is capable of estimating forest heights over the China continent with a satisfactory accuracy. Forth coming research on our model will focus on extending the model to the estimation of woody biomass. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present to the scientific community a new dataset derived from existing literature on soil respiration in Africa. The data has thus been obtained by searching for records in peer review papers and grey literature. The main search engines used are: Scientific Citation Index (SCI) database, ISI Science web and Google scholar. This data description paper has greatly advanced the number of data points on soil respiration in Africa from 4 in 2010 to 62 in 2014. The new data points are culled from 47 peer review publications and grey literature reports. The data lends its self to a lot of possible analytical methods such as correlation analysis, multiple linear regressions, artificial neural network analysis and process base modeling. The overall conclusion that can be drawn here is that this paper has greatly advanced the availability of soil respiration data in Africa by presenting all the available records that before now were only reported in different studies. [ABSTRACT FROM AUTHOR]

Forest plays an important role in global carbon, hydrological and atmospheric cycles and provides a wide range of valuable ecosystem services. Timely and accurate forest-type mapping is an essential topic for forest resource inventory supporting forest management, conservation biology and ecological restoration. Despite efforts and progress having been made in forest cover mapping using multi-source remotely sensed data, fine spatial, temporal and spectral resolution modeling for forest type distinction is still limited. In this paper, we proposed a novel spatial-temporal-spectral fusion framework through spatial-spectral fusion and spatial-temporal fusion. Addressing the shortcomings of the commonly-used spatial-spectral fusion model, we proposed a novel spatial-spectral fusion model called the Segmented Difference Value method (SEGDV) to generate fine spatial-spectra-resolution images by blending the China environment 1A series satellite (HJ-1A) multispectral image (Charge Coupled Device (CCD)) and Hyperspectral Imager (HSI). A Hierarchical Spatiotemporal Adaptive Fusion Model (HSTAFM) was used to conduct spatial-temporal fusion to generate the fine spatial-temporal-resolution image by blending the HJ-1A CCD and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The spatial-spectral-temporal information was utilized simultaneously to distinguish various forest types. Experimental results of the classification comparison conducted in the Gan River source nature reserves showed that the proposed method could enhance spatial, temporal and spectral information effectively, and the fused dataset yielded the highest classification accuracy of 83.6% compared with the classification results derived from single Landsat-8 (69.95%), single spatial-spectral fusion (70.95%) and single spatial-temporal fusion (78.94%) images, thereby indicating that the proposed method could be valid and applicable in forest type classification. [ABSTRACT FROM AUTHOR]

Agricultural mechanization is an important factor to improve the green total factor productivity of the planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of the output-oriented distance function to measure the green total factor productivity of China's planting industry based on net carbon sinks, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China's planting industry. The main findings of this paper are as follows: (1) Agricultural mechanization can promote the planting green total factor productivity significantly, and this basic conclusion is still robust after using instrumental variables and sub sample regression. (2) The path of agricultural mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover, while the mechanisms of operation scale expansion, factor allocation optimization and technical efficiency change are not significant. (3) With the improvement in the mechanization level, the promotion effect of mechanization on planting GTFP will become clearer. Given these findings, the paper adds considerable value to the empirical literature and provides various policy and practical implications. [ABSTRACT FROM AUTHOR]

The traditional field-based measurements of carbon dioxide (pCO2) for inland waters are a snapshot of the conditions on a particular site, which might not adequately represent the pCO2 variation of the entire lake. However, these field measurements can be used in the pCO2 remote sensing modeling and verification. By focusing on inland waters (including lakes, reservoirs, rivers, and streams), this paper reviews the temporal and spatial variability of pCO2 based on published data. The results indicate the significant daily and seasonal variations in pCO2 in lakes. Rivers and streams contain higher pCO2 than lakes and reservoirs in the same climatic zone, and tropical waters typically exhibit higher pCO2 than temperate, boreal, and arctic waters. Due to the temporal and spatial variations of pCO2, it can differ in different inland water types in the same space-time. The estimation of CO2 fluxes in global inland waters showed large uncertainties with a range of 1.40–3.28 Pg C y−1. This paper also reviews existing remote sensing models/algorithms used for estimating pCO2 in sea and coastal waters and presents some perspectives and challenges of pCO2 estimation in inland waters using remote sensing for future studies. To overcome the uncertainties of pCO2 and CO2 emissions from inland waters at the global scale, more reliable and universal pCO2 remote sensing models/algorithms will be needed for mapping the long-term and large-scale pCO2 variations for inland waters. The development of inverse models based on dissolved biogeochemical processes and the machine learning algorithm based on measurement data might be more applicable over longer periods and across larger spatial scales. In addition, it should be noted that the remote sensing-retrieved pCO2/the CO2 concentration values are the instantaneous values at the satellite transit time. A major technical challenge is in the methodology to transform the retrieved pCO2 values on time scales from instant to days/months, which will need further investigations. Understanding the interrelated control and influence processes closely related to pCO2 in the inland waters (including the biological activities, physical mixing, a thermodynamic process, and the air–water gas exchange) is the key to achieving remote sensing models/algorithms of pCO2 in inland waters. This review should be useful for a general understanding of the role of inland waters in the global carbon cycle. [ABSTRACT FROM AUTHOR]

This paper presents a new method for constructing quickly and automatically precision tree models from point clouds of the trunk and branches obtained by terrestrial laser scanning. The input of the method is a point cloud of a single tree scanned from multiple positions. The surface of the visible parts of the tree is robustly reconstructed by making a flexible cylinder model of the tree. The thorough quantitative model records also the topological branching structure. In this paper, every major step of the whole model reconstruction process, from the input to the finished model, is presented in detail. The model is constructed by a local approach in which the point cloud is covered with small sets corresponding to connected surface patches in the tree surface. The neighbor-relations and geometrical properties of these cover sets are used to reconstruct the details of the tree and, step by step, the whole tree. The point cloud and the sets are segmented into branches, after which the branches are modeled as collections of cylinders. From the model, the branching structure and size properties, such as volume and branch size distributions, for the whole tree or some of its parts, can be approximated. The approach is validated using both measured and modeled terrestrial laser scanner data from real trees and detailed 3D models. The results show that the method allows an easy extraction of various tree attributes from terrestrial or mobile laser scanning point clouds. [ABSTRACT FROM AUTHOR]

The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer(MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete Radiative Transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model and resulting in an inference of land surface parameters, such as effective Leaf Area Index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with eight years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three other widely-used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, the MISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements (r2 > 0.8), as well as the lowest average RMSE (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases (r2 > 0.85); the RMSE is lower by 4-5%; and the bias is 2% and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground-based LAI (average r2 = 0.76), but an underestimate of LAI for optically-thick canopies due to saturation (average RMSE = 2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument. [ABSTRACT FROM AUTHOR]

The 'Hestia Project' uses a bottom-up approach to quantify fossil fuel CO2 (FFCO2) emissions spatially at the building/street level and temporally at the hourly level. Hestia FFCO2 emissions are provided in the form of a group of sector-specific vector layers with point, line and polygon sources to support carbon cycle science and climate policy. Application to carbon cycle science, in particular, requires regular gridded data in order to link surface carbon fluxes to atmospheric transport models. However, the heterogeneity and complexity of FFCO2 sources within regular grids is sensitive to spatial resolution. From the perspective of a data provider, we need to find a balance between resolution and data volume so that the gridded data product retains the maximum amount of information content while maintaining an efficient data volume. The Shannon entropy determines the minimum bits that are needed to encode an information source and can serve as a metric for the effective information content. In this paper, we present an analysis of the Shannon entropy of gridded FFCO22emissions with varying resolutions in four Hestia study areas and find: (1) the Shannon entropy increases with smaller grid resolution until it reaches a maximum value (the max-entropy resolution); (2) total emissions (the sum of several sector-specific emission fields) show a finer max-entropy resolution than each of the sector-specific fields; (3) the residential emissions show a finer max-entropy resolution than the commercial emissions; (4) the max-entropy resolution of the onroad emissions grid is closely correlated to the density of the road network. These findings suggest that the Shannon entropy can detect the information effectiveness of the spatial resolution of gridded FFCO2 emissions. Hence, the resolution-entropy relationship can be used to assist in determining an appropriate spatial resolution for urban CO2 flux studies. We conclude that the optimal spatial resolution for providing Hestia total FFCO2 emissions products is centered around 100 m, at which the 2 emissions data can not only fully meet the requirement of urban flux integration, but also be effectively used in understanding the relationships between 2 emissions and various social-economic variables at the U.S. census block group level. [ABSTRACT FROM AUTHOR]

This paper presents the methodology and results of two ecological-based net ecosystem production (NEP) regression tree models capable of up scaling measurements made at various flux tower sites throughout the U.S. Great Plains. Separate grassland and cropland NEP regression tree models were trained using various remote sensing data and other biogeophysical data, along with 15 flux towers contributing to the grassland model and 15 flux towers for the cropland model. The models yielded weekly mean daily grassland and cropland NEP maps of the U.S. Great Plains at 250 m resolution for 2000-2008. The grassland and cropland NEP maps were spatially summarized and statistically compared. The results of this study indicate that grassland and cropland ecosystems generally performed as weak net carbon (C) sinks, absorbing more C from the atmosphere than they released from 2000 to 2008. Grasslands demonstrated higher carbon sink potential (139 g C.m-2.year-1) than non-irrigated croplands. A closer look into the weekly time series reveals the C fluctuation through time and space for each land cover type. [ABSTRACT FROM AUTHOR]

Vegetation is an important part of the terrestrial ecosystem and plays a vital role in the global carbon cycle. Traditional remote sensing methods have certain limitations in vegetation monitoring, and the development of GNSS-R (Global Navigation Satellite System-Reflectometry) technology provides a new and complimentary method. With the CYGNSS (Cyclone Global Navigation Satellite System) launch and the increased data acquisition, the use of spaceborne GNSS-R for vegetation monitoring has become a research hotspot. However, due to the complex characteristics of vegetation, its application in this field is still in the exploratory research stage. On the basis of reviewing the current research status, this paper points out the weak links of this technology in terms of polarization and observation geometry. Combined with the microwave vegetation scattering model, this paper analyzes the full polarization bistatic scattering characteristics of vegetation and points out the influence of vegetation parameters (density, water content, and vegetation diameters). The potential feasibility of polarization GNSS-R and future development trends of GNSS-R technology in quantitative retrieval (such as vegetation water content and biomass) are also discussed. [ABSTRACT FROM AUTHOR]

Forest height is of great significance in analyzing the carbon cycle on a global or a local scale and in reconstructing the accurate forest underlying terrain. Major algorithms for estimating forest height, such as the three-stage inversion process, are depending on the random-volume-over-ground (RVoG) model. However, the RVoG model is characterized by a lot of parameters, which influence its applicability in forest height retrieval. Forest density, as an important biophysical parameter, is one of those main influencing factors. However, its influence to the RVoG model has been ignored in relating researches. For this paper, we study the applicability of the RVoG model in forest height retrieval with different forest densities, using the simulated and real Polarimetric Interferometric SAR data. P-band ESAR datasets of the European Space Agency (ESA) BioSAR 2008 campaign were selected for experiments. The test site was located in Krycklan River catchment in Northern Sweden. The experimental results show that the forest density clearly affects the inversion accuracy of forest height and ground phase. For the four selected forest stands, with the density increasing from 633 to 1827 stems/Ha, the RMSEs of inversion decrease from 4.6 m to 3.1 m. The RVoG model is not quite applicable for forest height retrieval especially in sparsely vegetated areas. We conclude that the forest stand density is positively related to the estimation accuracy of the ground phase, but negatively correlates to the ground-to-volume scattering ratio. [ABSTRACT FROM AUTHOR]

Water use efficiency (WUE) is a useful indicator to illustrate the interaction of carbon and water cycles in terrestrial ecosystems. MODIS gross primary production (GPP) and evapotranspiration (ET) products have been used to analyze the spatial and temporal patterns of WUE and their relationships with environmental factors at regional and global scales. Although MODIS GPP and ET products have been evaluated using eddy covariance flux measurements, the accuracy of WUE estimated from MODIS products has not been well quantified. In this paper, we evaluated WUE estimated from MODIS GPP and ET products against eddy covariance measurements of GPP and ET during 2003-2008 at eight sites of the Chinese flux observation and research network (ChinaFLUX) and conducted sensitivity analysis to investigate the possible key contributors to the bias of MODIS products. Results show that MODIS products underestimate eight-day water use efficiency in four forest ecosystems and one cropland ecosystem with the bias from -0.36-2.28 g.C.kg-1 H2O, while overestimating it in three grassland ecosystems with the bias from 0.26-1.11 g.C.kg-1 H2O. Mean annual WUE was underestimated by 14%-54% at four forest sites, 45% at one cropland site and 7% at an alpine grassland site, but overestimated by 66% and 9% at a temperate grassland site and an alpine meadow site, respectively. The underestimation of WUE by MODIS data results from underestimated GPP and overestimated ET at four forest sites, while MODIS WUE values are significantly overvalued mainly due to underestimated ET in the three grassland ecosystems. The maximum light use efficiency and fraction of photosynthetically-active radiation (FPAR) were the two most sensitive factors to the estimation of WUE derived from the MODIS GPP and ET algorithms. The error in meteorological data partly caused the overestimation of ET and accordingly underestimation in WUE in subtropical and tropical forests. The bias of MODIS-produced WUE was also derived from the uncertainties in eddy flux data due to gap-filling processes and unbalanced surface energy issue. Their contributions to the uncertainty in estimated WUE at both eight-day and annual scales still need to be further quantified. [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB), which plays an important role in the study of global carbon cycle, is one of the most important indicators in forest resource monitoring. Thus, how to estimate and map regional forest AGB quickly and accurately attracts more interests of researchers. Tomographic SAR (TomoSAR) is an advanced SAR technique developed in recent years, which has a wide range application in forest AGB estimation. In this paper, we proposed a multi-feature-based modeling method to estimate forest AGB by fitting backscattered power of TomoSAR vertical profile. The procedure of the proposed method includes four parts: (1) Processing TomoSAR data to obtain the backscattered power of vertical profile. (2) Fitting the backscattered power of the vertical profile. (3) Analyzing the fitted backscattered power distribution characteristic of the vertical profile. (4) Extracting the TomoSAR vertical profile features according to the forest AGB measurement factors based on the dendrometry theory. In this paper, we proposed two new features like the forest average height weighted by backscattered power (BPFAH) and the total length of the backscattered power curve (LBPC) as supplement features to estimate forest AGB by TomoSAR technique. We also used the traditional TomoSAR features including backscattered power at specific height layer of vertical power profile (BPV) and forest average height (FAH) for AGB estimation. After the feature selection, the selected features and the ground field data of the forest AGB were used for regression and modeling. Then the forest AGB was estimated and the accuracy was validated. The results showed that the accuracy of proposed method is 90.73%, and RMSE is 42.45 t/ha. Finally, we discussed the performance of our proposed method compared with traditional methods. [ABSTRACT FROM AUTHOR]

Current satellite remote sensing methods struggle to detect and map forest degradation, which is a critical issue as it is likely a major and growing source of carbon emissions and biodiveristy loss. TanDEM-X InSAR phase height ( h ϕ ) is a promising variable for measuring forest disturbances, as it is closely related to the mean canopy height, and thus should decrease if canopy trees are removed. However, previous research has focused on relatively flat terrains, despite the fact that much of the world's remaining tropical forests are found in hilly areas, and this inevitably introduces artifacts in sideways imaging systems. In this paper, we find a relationship between h ϕ and aboveground biomass change in four selectively logged plots in a hilly region of central Gabon. We show that minimising multilooking prior to the calculation of h ϕ strengthens this relationship, and that degradation estimates across steep slopes in the surrounding region are improved by selecting data from the most appropriate pass directions on a pixel-by-pixel basis. This shows that TanDEM-X InSAR can measure the magnitude of degradation, and that topographic effects can be mitigated if data from multiple SAR viewing geometries are available. [ABSTRACT FROM AUTHOR]

Natural gas hydrate is one of the most important clean energies and part of carbon cycle, due to the least carbon content. Natural gas hydrates depend on high pressure and low temperatures, located under seabed or permafrost. Small changes in temperature and pressure may lead gas hydrates to separate into water and gas, commonly as methane. As a powerful greenhouse gas, methane is much stronger than carbon dioxide. Therefore, it is necessary to detect the gas hydrates stable zone (GHSZ) before the methane gas escapes from GHSZ. Marine controlled source electromagnetic method (CSEM) is a useful tool to detect gas hydrate in offshore. The results from 3D CSEM method are a resistivity cube to describe the distribution of gas hydrates. In order to study the detectability of CSEM method, we simulate the sensitivity and resolution of marine CSEM synthetic data. By using the sensitivity and resolution, a simple statement may be quickly judged on the existence and occurrence range of the natural gas hydrate. In this paper, we compare the resolution of marine CSEM method with various transverse resistance. This information may help researchers find out whether the GHSZ exists or not. [ABSTRACT FROM AUTHOR]

Remotely sensed vegetation indices (VIs) have been widely used to estimate the aboveground biomass (AGB) carbon stock of coastal wetlands by establishing Vis-related linear models. However, these models always have high uncertainties due to the large spatial variation and fragmentation of coastal wetlands. In this paper, an efficient coastal wetland AGB model for the Bohami Rim coastal wetlands was presented based on multiple data sets. The model was developed statistically with 7 independent variables from 23 metrics derived from remote sensing, topography, and climate data. Compared to previous models, it had better performance, with a root mean square error and r value of 188.32 g m−2 and 0.74, respectively. Using the model, we firstly generated a regional coastal wetland AGB map with a 10 m spatial resolution. Based on the AGB map, the AGB carbon stock of the Bohai Rim coastal wetland was 2.11 Tg C in 2019. The study demonstrated that integrating emerging high spatial resolution multi-remote sensing data and several auxiliary metrics can effectively improve VIs-based coastal wetland AGB models. Such models with emerging freely available data sets will allow for the rapid monitoring and better understanding of the special role that "blue carbon" plays in global carbon cycle. [ABSTRACT FROM AUTHOR]

Water use efficiency (WUE) is a key index for understanding the ecosystem of carbon–water coupling. The undistinguishable carbon–water coupling mechanism and uncertainties of indirect methods by remote sensing products and process models render challenges for WUE remote sensing. In this paper, current progress in direct and indirect methods of WUE estimation by remote sensing is reviewed. Indirect methods based on gross primary production (GPP)/evapotranspiration (ET) from ground observation, processed models and remote sensing are the main ways to estimate WUE in which carbon and water cycles are independent processes. Various empirical models based on meteorological variables and remote sensed vegetation indices to estimate WUE proved the ability of remotely sensed data for WUE estimating. The analytical model provides a mechanistic opportunity for WUE estimation on an ecosystem scale, while the hypothesis has yet to be validated and applied for the shorter time scales. An optimized response of canopy conductance to atmospheric vapor pressure deficit (VPD) in an analytical model inverted from the conductance model has been also challenged. Partitioning transpiration (T) and evaporation (E) is a more complex phenomenon than that stated in the analytic model and needs a more precise remote sensing retrieval algorithm as well as ground validation, which is an opportunity for remote sensing to extrapolate WUE estimation from sites to a regional scale. Although studies on controlling the mechanism of environmental factors have provided an opportunity to improve WUE remote sensing, the mismatch in the spatial and temporal resolution of meteorological products and remote sensing data, as well as the uncertainty of meteorological reanalysis data, add further challenges. Therefore, improving the remote sensing-based methods of GPP and ET, developing high-quality meteorological forcing datasets and building mechanistic remote sensing models directly acting on carbon–water cycle coupling are possible ways to improve WUE remote sensing. Improvement in direct WUE remote sensing methods or remote sensing-driven ecosystem analysis methods can promote a better understanding of the global ecosystem carbon–water coupling mechanisms and vegetation functions–climate feedbacks to serve for the future global carbon neutrality. [ABSTRACT FROM AUTHOR]

A massive dust storm formed over the Sahara Desert in June 2020. The African dust cloud, which traveled over the tropical Atlantic's main development region for hurricanes, resulted in the highest aerosol optical thickness (AOT) for the past two decades. Dust particles contained in dust clouds are at some point deposited on the ocean surface, impacting the ocean biogeochemistry through the supply of nutrients. Although there are remote sensing systems that can map the AOT, the locations of the aerosol particles deposited on the ocean surface remain unknown quantities with remote sensing measurements. In addition, the supplied nutrients are not static and are displaced by ocean currents. Nutrients trigger the phytoplankton (algae) blooms, which form a film on the ocean surface and affect the ocean surface tension. The change in ocean surface tension causes a local decrease of ocean surface roughness over the areas covered with phytoplankton. Bistatic radar data from the CYclone Global Navigation Satellite System (CYGNSS) mission can detect changes in the ocean surface roughness, expressed as an increase in reflectivity when the surface becomes smoother. Therefore, decreased ocean surface roughness correlated with a recent dust storm represents a key indicator of the presence of phytoplankton. In this paper, we present for the first time the capability of bistatic radar measurements to provide an effective tool to map information of areas covered with phytoplankton, establishing bistatic radar as the most reliable remote sensing tool for detecting phytoplankton blooms and monitoring their presence across the ocean surface. We present the analysis of low ocean roughness signatures in the bistatic radar measurements from the CYGNSS mission observed in the Gulf of Mexico after the Sahara's dust storm circulation from Africa to the American continent from May to July 2020. CYGNSS data offer an unprecedented spatial and temporal coverage that allows for the analysis of those signatures at time scales of 1-day, robust to the presence of clouds and dust clouds. The described capability benefits the improvement of models, promoting a better constraint of the supply of dust into the ocean surface and a better understanding of the excess of nutrients that triggers the phytoplankton blooms. This new bistatic radar application enhances our understanding on the role of dust storms on ocean biogeochemistry and the global carbon cycle. [ABSTRACT FROM AUTHOR]