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Many nodes of a complex network in the real world belong to multiple communities. Research on the detection of overlapping communities with common nodes can help us understand the topology of network and extract information hidden in the network. Considering the problems of many existing overlapping community detection methods, such as high time complexity or poor division results, we propose an overlapping community detection method based on local influence and similarity (ISOCD). The ISOCD method selects seed nodes based on local influence, follows a greedy strategy, takes the fitness function of similarity as the optimization goal, and finally optimizes and merges the detected communities. In experiments, the method we propose has good community division effects both on the real-world and synthetic networks, and has low time complexity. [ABSTRACT FROM AUTHOR]

Surface temperatures are mechanistically linked to vegetation biophysical and physiological processes. Although remote sensing in the thermal infrared (TIR) domain can offer novel insights into the impacts of changing surface temperatures on vegetation, the transformative potential of remote sensing for plant ecology has not yet been realized.Remotely sensed surface temperatures can be used to derive stomatal behaviour and identify stressful environmental conditions in near‐real time. Plant species, traits and structural characteristics can be evaluated with high spectral resolution TIR emissivity.Beyond canopy scales, thermal remote sensing can enhance the inferences obtained from manipulative experiments and empirical evidence, providing unique insight into shifts in species ranges and phenology with changing climate conditions.Scaling leaf traits, canopy structure and regional patterns require an integrated understanding of both process and technology. Theory linking surface temperatures to vegetation dynamics is summarized from an energy balance perspective. We outline scaling considerations including the impacts of morphology on leaf energy balance, canopy structure influences on convective heat exchange and potential confounding impacts of non‐vegetated surfaces.Synthesis. We introduce a unifying framework to link leaf to globe through thermal remote sensing. Recent and emerging advances in sensors, data availability and analytics, together with synergies between TIR remote sensing and other data sources, present a timely opportunity for ecologists to advance our understanding of plant physiology, ecology and biogeography with thermal remote sensing. [ABSTRACT FROM AUTHOR]

• A comprehensive assessment of the three major droughts' impacts on Amazon forests. • The 2015/16 drought is the most severe meteorological drought since 2000. • Drought in 2015/16 showed lagging impacts on photosynthesis, greenness, and leaf area. • Photosynthesis, greenness, and leaf area had similar suppression in 2010 and 2015/16. • Canopy water content exhibited more widespread and severe drought impacts in 2015/16. The Amazon forests play an integral role in the global carbon cycle and have a substantial impact on Earth's climate. However, it is increasingly susceptible to the effects of prolonged droughts, exacerbated by climate change and human activities. This vulnerability underscores the importance of understanding the forests' reaction to such environmental stressors. Despite their significance, comprehensive cross-comparisons of the climate and vegetation responses during the 2005, 2010, and 2015/2016 drought episodes are not well-established. Here we utilize a range of gridded vegetation and climate datasets—including leaf area index (LAI), solar-induced chlorophyll fluorescence (SIF), enhanced vegetation index (EVI), vegetation optical depth (VOD), self-calibrating Palmer drought severity index (scPDSI), precipitation (P), land surface temperature (LST), and photosynthetically active radiation (PAR)—to thoroughly assess the climate and vegetation response to these three drought events. Our findings reveal that the extent of drought inhibition in the Amazon forests was 74.7 % in 2015, increasing to 81.3 % in 2016, a significant escalation from 49.6 % in 2005 and 57.7 % in 2010. The effects of these three droughts on vegetation varied in both physiological and structural aspects. The Amazon forests' photosynthetic activity, greenness, and leaf area experienced comparable suppression in 2010 and 2015/2016 droughts. However, canopy water content exhibited more extensive and severe impacts during the 2015/2016 drought. Our findings indicate that varying sensitivities to water deficit and solar radiation lead to diverse spatial patterns and intensities of vegetation response, highlighting the complex dynamics of the Amazon forests under drought stress. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power. Graph neural network methods within deep learning have shown remarkable capabilities in processing graph-structured data, such as social networks and traffic networks. As a result, they have garnered significant attention from researchers.However, real-world data often face challenges like data sparsity and missing labels, which can hinder the performance and generalization ability of graph convolutional neural networks. To overcome these challenges, our research aims to effectively extract the hidden features and topological information of graph convolutional neural networks. We propose an innovative model called Adaptive Feature and Topology Graph Convolutional Neural Network (AAGCN). By incorporating an adaptive layer, our model preprocesses the data and integrates the hidden features and topological information with the original data’s features and structure. These fused features are then utilized in the convolutional layer for training, significantly enhancing the expressive power of graph convolutional neural networks.To evaluate the effectiveness of the adaptive layer in the AAGCN model, we conducted node classification experiments on real datasets. The results validate its ability to address data sparsity and improve the classification performance of graph convolutional neural networks.In conclusion, our research primarily focuses on addressing data sparsity and missing labels in graph convolutional neural networks. The proposed AAGCN model, which incorporates an adaptive layer, effectively extracts hidden features and topological information, thereby enhancing the expressive power and classification performance of these networks. [ABSTRACT FROM AUTHOR]

Drylands cover about 40% of the terrestrial surface and are sensitive to climate change, but their relative contributions to global vegetation greening and productivity increase in recent decades are still poorly known. Here, by integrating satellite data and biosphere modeling, we showed that drylands contributed more to global gross primary productivity (GPP) increase (65% ± 16%) than to Earth greening (33% ± 15%) observed during 1982–2015. The enhanced productivity per unit leaf area, i.e., light-use efficiency (LUE), was the mechanism behind this pattern. We also found that LUE was more sensitive to soil moisture than to atmospheric vapor pressure deficit (VPD) in drylands, while the opposite was observed (i.e., LUE was more sensitive to VPD) in humid areas. Our findings suggest the importance of using different moisture stress metrics in projecting the vegetation productivity changes of dry versus humid regions and highlight the prominent role of drylands as key controllers of the global carbon cycle. [ABSTRACT FROM AUTHOR]

Flash drought is an extreme phenomenon, characterized by unusually rapid intensification of drought severity, with strong impacts on plant growth especially for crops. However, it's unclear how hydrometeorological changes contribute to flash drought and how vegetation physiology, greenness, and productivity respond to flash drought. In this study, we developed an multivariate integrated framework for flash drought identification using a regression model based on principal-components analysis (PCA): the PCA regression flash drought (PRFD) model. Three criteria are specified to emphasize the rapid intensification of drought and its impacts on vegetation growth and water resources. We applied our new model in two geographical units with different climates and hydrology: one is dominated by agriculture and subject to natural drought (the Loess Plateau) and the other, a natural region with infrequent drought (the Qinling Mountains). We found that high frequency of flash droughts is most likely to occur in the eastern and central Loess Plateau and part of the Qinling Mountains. However, in relatively humid areas, flash drought shows strong intensity such as the Qinling Mountains and western and eastern edges of the Loess Plateau. Trend analysis indicates that PRFD has increased frequency, longer duration, and stronger intensity since the 1990s in both regions. From an ecological perspective, PRFD also showed a spatial pattern consistent with values of vegetation-related proxies that were below the long-term average, demonstrating vegetation transpiration, normalized-difference vegetation index (NDVI), gross primary productivity have obvious feedback on flash drought events. The Loess Plateau's NDVI responded immediately to flash drought, versus a 1-month lag in the Qinling Mountains. Because our proposed framework integrates multiple aspects of drought information, it can be applied in areas outside the study region according to regional hydrometeorological conditions. This has significant implications for improving agricultural management and forecasting future severe impacts of flash drought on plant growth. • A new integrated framework is proposed to identify flash drought (FD) events. • FD with greater frequency, longer pentads, and stronger intensity since 1990s. • Vegetation structure and physiological function respond rapidly to FD. • The response time of plant activity to FD is different in diverse climate-water regions. [ABSTRACT FROM AUTHOR]

As a common natural disaster, drought can significantly affect the agriculture productivity and human life. Compared to Southeast China, Northwest China is short of water year-round and is the most frequent drought disaster area in China. Currently, there are still many controversial issues in drought monitoring of Northwest China in recent decades. To further understand the causes of changes in drought in Northwest China, we chose Shaanxi, Gansu, and Ningxia provinces (SGN) as our study area. We compared the spatiotemporal characteristics of drought intensity and frequency in Northwest China from 2003 to 2020 showed by the Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Normalized Vegetation Supply Water Index (NVSWI), Soil Moisture Condition Index (SMCI), and Soil Moisture Agricultural Drought Index (SMADI). All of these indices showed a wetting trend in the SGN area from 2003 to 2020. The wetting trend of the VCI characterization is the most obvious (R2 = 0.9606, p < 0.05): During the period 2003–2020, the annual average value of the VCI in the SGN region increased from 28.33 to 71.61, with a growth rate of 153.57%. The TCI showed the weakest trend of wetting (R2 = 0.0087), with little change in the annual average value in the SGN region. The results of the Mann–Kendall trend test of the TCI indicated that the SGN region experienced a non-significant (p > 0.05) wetting trend between 2003 and 2020. To explore the effectiveness of different drought indices, we analyzed the Pearson correlation between each drought index and the Palmer Drought Severity Index (PDSI). The PDSI can not only consider the current water supply and demand situation but also consider the impact of the previous dry and wet conditions and their duration on the current drought situation. Using the PDSI as a reference, we can effectively verify the performance of each drought index. SPI-12 showed the best correlation with PDSI, with R values greater than 0.6 in almost all regions and p values less than 0.05 within one-half of the study area. SMADI had the weakest correlation with PDSI, with R values ranging −0.4~−0.2 and p values greater than 0.05 in almost all regions. The results of this study clarified the wetting trend in the SGN region from 2003 to 2020 and effectively analyzed the differences in each drought index. The frequency, duration, and severity of drought are continuously reduced; this helps us to have a more comprehensive understanding of the changes in recent decades and is of significance for the in-depth study of drought disasters in the future. [ABSTRACT FROM AUTHOR]

As the most frequent and longest-lasting climate-related disasters, droughts are complex processes caused by hydrological imbalance, and have significant effects on ecosystem function and socioeconomic development. However, most conventional satellite-based drought indices and combined drought indices are mechanically constrained by the fact that they were developed and evaluated for a specific climate or critical ecological region, especially in China. A comprehensive drought index should be developed for extensive application in different climate-hydrology regions. To address this problem, an optimised principal component analysis method (PCA-RF) was proposed to support drought assessment across the transitional area between the humid and arid regions in central China, including the Loess Plateau (LP) and Qinling Mountains (QL). This method eliminated the causal relationships of non-linear changes for multi-aspect parameters such as land surface temperature, precipitation and soil moisture, and was used in developing a new index named "Meteorological drought index by PCA-RF methods (PMDI)". The results showed that PMDI had higher correlations and lower root mean square error than other drought indices (established using the empirical-weights method) with the 1-month standardized precipitation evapotranspiration index (SPEI-1). That means PMDI performed well in meteorological drought monitoring. Additionally, the normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) were utilised to evaluate PMDI effectiveness in detecting vegetation response to drought, which represents agricultural drought. The results indicated that correlations between PMDI and vegetation indices (VIs) were stronger when severe or extreme droughts occurred, and that their relationships were affected by the high spatiotemporal heterogeneity in vegetation change over the LP and QL. Overall, PMDI was shown here to be a very suitable index for monitoring meteorological drought (short-term) and has effective action in capturing the response of vegetation signals to agriculture drought events (long-term). Given the excellent performance of PMDI in diverse climate-hydrology conditions of our study regions, we concluded that the PMDI has considerable potential as a drought monitoring methodology on a larger spatial scale. • A new drought index was developed (PMDI), which integrated multiple sources of information describing different aspects. • PMDI performed well in different climate-hydrology regions for short- and long-term drought monitoring. • PMDI has reasonably good potential applicability in drought monitoring on a larger spatial scale. [ABSTRACT FROM AUTHOR]

• Forest age was mapped at 30 m resolution using GEDI-derived forest height maps. • Climate and topographic data accounted for 23–52% in forest age mapping. • Logistic equation was the best in mapping forest age among stand growth equations. • Random forest was applicable to map forest age without classifying tree species. Forest age is a key parameter for estimating forest growth and carbon uptake and for forest management. Remote sensing provides indirect but useful information for mapping forest age at large scales. However, existing regional and global forest age products were generated at low spatial resolutions (often 1000 m) and are not useful for most forest stands in China that are smaller than 1000 m. This study aims to map forest age at the 30 m resolution based on forest height maps mainly derived from the Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data, and analyze the roles of auxiliary data including temperature, precipitation, slope, and aspect in forest age mapping. Forest age is defined as the average age of dominant tree species within a pixel. Five commonly-used stand growth equations and twelve machine learning methods were tested for their suitability for mapping forest age of different tree species. We found that the Logistic equation performed the best among the tested stand growth equations and the Random Forest (RF) was the best among the tested machine learning methods. According to RF, forest height contributed predominantly to the variance in forest age mapping, while temperature, precipitation, slope, and aspect also had an overall non-negligible and variable contribution among different tree species. By integrating the climate and topographical variables, RF was applicable for forest age mapping without classifying the tree species. These results show that forest height maps derived from space-borne lidar data such as GEDI and ICESat-2 data are highly useful for mapping forest stand age, and the methodology developed in this study highlights a perspective for generating national and global forest age products at a high spatial resolution. [ABSTRACT FROM AUTHOR]

• A modified isotope-based method was developed to estimate the evapotranspiration partitioning, and it can be applied on various time scales. • The modified method and the traditional method are equivalent at a 15-minute time scale. • The modified method is capable of partitioning evapotranspiration at high-frequency and distributed the high sensitivity parameter δ ET into other three less sensitive parameters. • The high-frequency method is advantageous in avoiding the extrapolation of the keeling plot regression line, and enabling the calculation of confidence interval of evapotranspiration partition. • The high-frequency method might be useful when investigating evapotranspiration partitioning under short-term extreme weather events and flush agricultural irrigation. To better understand water and energy cycles, numerous efforts to partition evapotranspiration (ET) into evaporation (E) and transpiration (T) have been made over the recent half century. One of the analytical methods is the isotopic approach. The isotopic composition of ET (δ ET) is a crucial parameter in the traditional isotope-based ET partitioning model, which however, has considerable uncertainty and high sensitivity. Here we proposed a modified T fraction in total ET (F T) calculation using Keeling plot slope (k), the atmospheric vapor concentration (C v), and the isotopic composition of atmospheric vapor (δ v), to avoid the direct use of δ ET. Following the traditional method, we used the Craig-Gordon model for the isotopic composition of evaporation (δ E) and chamber method for the isotopic composition of transpiration (δ T) in our modified method. The modified F T calculation method (F T (m)) can be applied at a 15-min time scale using the average values (F Ti (mp)) and at a 1 Hz time scale for high-frequency method (F Ti). The modified method was verified by both theoretical derivations and field observations. F Ti (mp) was equivalent to those using the traditional isotopic method at a 15-min time scale. However, F Ti eliminated the highly sensitive parameter δ ET , and redistributed the sensitivity of δ ET into three less sensitive parameters. Additionally, F Ti has two main advantages. First, the high-frequency method avoids the extrapolation of the Keeling plot regression line intercept. Second, the high-frequency method can produce a 95% confidence interval of F T in a measurement cycle (e.g., 15 min). The calculated confidence interval was different from that of traditional uncertainty analysis. The high-frequency method might be useful when investigating evapotranspiration partitioning under short-term extreme weather events and flush agricultural irrigation. [ABSTRACT FROM AUTHOR]