Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

Recently, ensemble multiple deep learning (DL) classifiers has been reported to be an effective method for improving remote sensing classification accuracy. Although these approaches still follow the conventional pattern of inputting instance features and outputting corresponding classes, they often overlook the intrinsic relationships between pixels beyond their spatial features. As a result, the diversity in the ensemble classification results primarily relies on different DL models. However, training the DL models consumes a significant amount of time, and training multiple networks not only incurs additional time costs but also affects the overall efficiency. To address this, a new approach has been proposed in this paper, which takes advantage of the relationships between pixels and their combinations to generate diverse classification results. It’s a novel ensemble classification framework, termed as the Doublet-Based Ensemble Classification Framework (DBECF), which eliminates the need for multiple classifiers. The DBECF starts by utilizing the training set to combine different samples to generate doublets. Then, features are assigned to these doublets through an exponentiation operation, resulting in a doublet training set. Using both the original training set and the derived doublet datasets, the DBECF is trained. For each input pixel, the DBECF produces multiple classification results, which are then integrated to obtain a more accurate output. To validate the proposed approach, experiments were conducted on three datasets, including multispectral images, hyperspectral images, and time series images. The maximum accuracies achieved by DBECF on the three datasets are 87.80 %, 97.71 %, and 83.51 %, respectively. In comparison to the contrastive methods, the incremental improvements in accuracy are 3.73 %, 7.66 %, and 9.16 %, respectively. The experimental results indicate that no matter using DL or non-deep learning for training, our proposed framework achieves progress on accuracy improvement outperforming classifications using comparative approach that based on single instance. This research provides a new perspective on the combination of DL and ensemble learning, highlighting its important implications and practical value in enhancing classification accuracy and efficiency.

The vegetation and ecosystem in the source region of the Yangtze River and the Yellow River (SRYY) are fragile. Affected by climate change, extreme droughts are frequent and permafrost degradation is serious in this area. It is very important to quantify the drought-vegetation interaction in this area under the influence of climate-permafrost coupling. In this study, based on the saturated vapor pressure deficit (VPD) and soil moisture (SM) that characterize atmospheric and soil drought, as well as the Normalized Differential Vegetation Index (NDVI) and solar-induced fluorescence (SIF) that characterize vegetation greenness and function, the evolution of regional vegetation productivity and drought were systematically identified. On this basis, the technical advantages of the causal discovery algorithm Peter-Clark Momentary Conditional Independence (PCMCI) were applied to distinguish the response of vegetation to VPD and SM. Furthermore, this study delves into the response mechanisms of NDVI and SIF to atmospheric and soil drought, considering different vegetation types and permafrost degradation areas. The findings indicated that low SM and high VPD were the limiting factors for vegetation growth. The positive and negative causal effects of VPD on NDVI accounted for 47.88% and 52.12% of the total area, respectively. Shrubs were the most sensitive to SM, and the response speed of grassland to SM was faster than that of forest land. The impact of SM on vegetation in the SRYY was stronger than that of VPD, and the effect in the frozen soil degradation area was more obvious. The average causal effects of NDVI and SIF on SM in the frozen soil degradation area were 0.21 and 0.41, respectively, which were twice as high as those in the whole area, and SM dominated NDVI (SIF) changes in 62.87% (76.60%) of the frozen soil degradation area. The research results can provide important scientific basis and theoretical support for the scientific assessment and adaptation of permafrost, vegetation, and climate change in the source area and provide reference for ecological protection in permafrost regions.

The aerosol particles present in the atmospheric region mainly affect the climate radiative forcing directly by scattering & absorbing the sunlight. Also, it indirectly influences the formation of clouds, precipitation and acts as a considerable uncertainty in assessing Earth's radiation budget. Determination of aerosol type is significant in characterizing the aerosol role in the atmospheric processes, feedback, and climate models. This paper proposes two aerosol classification models, one based on the source and another based on the composition, to classify the aerosols using aerosol optical properties. The source based aerosol classification method helps to identify the sources which cause pollution in a particular region. Based on the results, proper control measures can be taken to reduce pollution. The composition based aerosol classification helps to identify the nature of aerosol types, such as absorbing or non-absorbing. This classification helps to study the climate of the Kanpur region. The aerosol data is taken from AERONET (AErosol RObotic NETwork) for the period 2002-2018 for the Kanpur region. The composition based aerosol classification model uses Single Scattering Albedo (SSA), Angstrom Exponent (AE), and Fine Mode Fraction (FMF) parameters to categorize aerosols based on their composition. The source based aerosol classification model classifies the aerosols based on values of AE and Aerosol Optical Depth (AOD) and describes the source of the aerosol particles. Knowledge of aerosol sources and compositions helps execute policies or controls to reduce aerosol concentrations. Machine learning algorithms, Nai center dot ve Bayes, K Nearest Neighbor, Decision Tree, Support Vector Machine, and Random Forest are used to validate classification schemes. The performance analysis of machine learning algorithms is compared using ten different metrics, and the results are also compared with the existing aerosol classification models. The results of the classification show that the source based aerosols of the desert and arid background and the composition based aerosols of types, Mixture Absorbing, Coarse absorbing (Dust), and Black Carbon are dominant over the Kanpur region during the study period considered. The Number of non -absorbing (scattering) type aerosols are least in the study region considered during the study period at all the seasons. It is found that the Random Forest and Decision Tree models outperform the other machine learning models considered and the existing classification models in terms of accuracy (99.55 %) and other performance metrics considered.(c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved.

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products' (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused areas over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T's causality is evenly distributed amongst all biomes with fractional covers between similar to 10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet's ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (S5P) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers.

High-resolution permafrost mapping is an important direction in permafrost research. Arxan is a typical area with permafrost degradation and is situated on the southern boundary of the permafrost region in Northeast China. With the help of Google Earth Engine (GEE), the maximum entropy classifier (MaxEnt) is used for permafrost mapping using the land surface temperature (LST) of different seasons, deviation from mean elevation (DEV), solar radiation (SR), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) as the characteristic variables. The prior data of permafrost distribution were primarily based on 201 borehole data and field investigation data. A permafrost probability (PP) distribution map with a resolution of 30 m was obtained. The receiver operating characteristic (ROC) curve was used to test the distribution results, with an area under the curve (AUC) value of 0.986. The results characterize the distribution of permafrost at a high resolution. Permafrost is mainly distributed in the Greater Khingan Mountains (GKM) in the research area, which run from the northeast to the southwest, followed by low-altitude area in the northwest. According to topographic distribution, permafrost is primarily found on slope surfaces, with minor amounts present in peaks, ridges, and valleys. The employed PP distribution mapping method offers a suggestion for high-resolution permafrost mapping in permafrost degradation areas.

Tropical high-Andean wetlands, locally known as 'bofedales', are key ecosystems sustaining biodiversity, carbon sequestration, water provision and livestock farming. Bofedales' contribution to dry season baseflows and sustaining water quality is crucial for downstream water security. The sensitivity of bofedales to climatic and anthropogenic disturbances is therefore of growing concern for watershed management. This study aims to understand seasonal water storage and release characteristics of bofedales by combining remote sensing analysis and ground-based monitoring for the wet and dry seasons of late 2019 to early 2021, using the glacierised Vilcanota-Urubamba basin (Southern Peru) as a case study. A network of five ultrasound loggers was installed to obtain discharge and water table data from bofedal sites across two headwater catchments. The seasonal extent of bofedales was mapped by applying a supervised machine learning model using Random Forest on imagery from Sentinel-2 and NASADEM. We identified high seasonal variability in bofedal area with a total of 3.5% and 10.6% of each catchment area, respectively, at the end of the dry season (2020), which increased to 15.1% and 16.9%, respectively, at the end of the following wet season (2021). The hydrological observations and bofedal maps were combined into a hydrological conceptual model to estimate the storage and release characteristics of the bofedales, and their contribution to runoff at the catchment scale. Estimated lag times between 1 and 32 days indicate a prolonged bofedal flow contribution throughout the dry season (about 74% of total flow). Thus, our results suggest that bofedales provide substantial contribution to dry season baseflow, water flow regulation and storage. These findings highlight the importance of including bofedales in local water management strategies and adaptation interventions including nature-based solutions that seek to support long-term water security in seasonally dry and rapidly changing Andean catchments.

The Qinghai-Tibet Plateau is rich in water resources with numerous lakes, rivers, and glaciers, and, as a source of many rivers in Central Asia, it is known as the Asian Water Tower. Under global climate change, it is critical to understand the current influencing factors on surface water area in this region. Although there are numerous studies on surface water mapping, they are still limited by temporal/spatial resolution and record length. Moreover, the complicated topographic condition makes it challenging to map the surface water accurately. Here, we proposed an automatic two-step annual surface water classification framework using long time-series Landsat images and topographic information based on the Google Earth Engine (GEE) platform. The results showed that the producer accuracy (PA) and user accuracy (UA) of the surface water map in the Qinghai-Tibet Plateau in 2020 were 99% and 90%, respectively, and the Kappa coefficient reached 0.87. Our dataset showed high consistency with high-resolution images, indicating that the proposed large-scale water mapping method has great application potential. Furthermore, a new annual surface water area dataset on the Qinghai-Tibet Plateau from 2000 to 2020 was generated, and its relationship with climate, vegetation, permafrost, and glacier factors was explored. We found that the mean surface water area was about 59 481 km(2), and there was a significant increasing trend (=322 km(2)/year, p < 0.01) during 2000-2020 in the plateau. Greening, warming, and wetting climate conditions contributed to the increase of surface water area. Active layer thickness and permafrost types may be the most related to the decrease of surface water area. This study provides important information for ecological assessment and protection of the plateau and promotes the implementation of sustainable development goals related to surface water resources.

Absorbing aerosols uniquely impact radiation, aerosol transport, and meteorology. This paper quantifies black carbon core and sulphate shell size and mass using decadal measurements of multi-spectral aerosol optical depth, single scattering albedo, and angstrom exponent from Aerosol Robotic Network stations located throughout East, Southeast, and South Asia, in connection with a MIE model. All sites are uniquely characterized into four types: urban, biomass burning, long-range transport, and clean. Unique size and mass probability distributions of both the core and shell are calculated within each classification. Well known urban, biomass burning, and clean sites are all properly identified. Furthermore, two unique sites previously thought to not have multiple characteristics are identified, with urban and biomass burning significant in Beijing and long-range transport significant in the otherwise clean South China Sea at Taiping Island. It is hoped that these results will allow for advances in attribution and radiative forcing studies. Plain Language Summary Black Carbon strongly absorbs visible radiation, leading to unique impacts on atmospheric radiation, climate, the water cycle, and PM2.5. This work attributes different aerosol source characteristics, and further specifies the size distribution and concentration of aerosol black carbon cores and refractive shells. This work uses measurements of aerosol optical depth, single scatter albedo, and angstrom exponent, across multiple different wavelengths of light, in combination with statistics and a MIE model (physical model of aerosol/radiation interaction) using a Core-Shell approximation. The results show that aerosols observed in East, Southeast, and South Asia can be uniquely classified into four types: urban, biomass burning, long-range transport, and clean. These results are consistent in terms of aerosol size and mass at each site within each type of characterization. Furthermore, two unique sites are identified in which a second characteristic occurs some significant fraction of every year, which otherwise was not known or previously identified in the literature. These results are expected to help enhance the understanding of attribution of aerosols, as well as provide specific size and mass details of the aerosols useful to improve radiative forcing models and aerosol impacts on climate change. Key Points Aerosols are categorized into biomass burning, urban, and long-range types over Asia using decadal long multi-spectral measurements Based on multiple Aerosol Robotic Network Single Scatter Albedo measurements and a MIE model, physical characteristics of different aerosol types are deduced Most aerosols are found to be mixed, with two sites having different characteristics during different times of the year

Studies in aerosol properties, types and sources in the Himalayas are important for atmospheric and climatic issues due to high aerosol loading in the neighboring plains. This study uses in situ measurements of aerosol optical and microphysical properties obtained during the Ganges Valley Aerosol eXperiment (GVAX) at Nainital, India over the period June 2011-March 2012, aiming to identify key aerosol types and mixing states for two particle sizes (PM1 and PM10). Using a classification matrix based on SAE vs. AAE thresholds (scattering vs. absorption Angstrom exponents, respectively), seven aerosol types are identified, which are highly dependent on particle size. An aerosol type named large/BC mix dominates in both PM1 (45.4%) and PM10 (46.9%) mass, characterized by aged BC mixed with other aerosols, indicating a wide range of particle sizes and mixing states. Small particles with low spectral dependence of the absorption (AAE < 1) account for 31.6% and BC-dominated aerosols for 14.8% in PM1, while in PM10, a large fraction (39%) corresponds to large/low-absorbing aerosols and only 3.9% is characterized as BC-dominated. The remaining types consist of mixtures of dust and local emissions from biofuel burning and display very small fractions. The main optical properties e.g. spectral scattering, absorption, single scattering albedo, activation ratio, as well as seasonality and dependence on wind speed and direction of identified types are examined, revealing a large influence of air masses originating from the Indo-Gangetic Plains. This indicates that aerosols over the central Himalayas are mostly composed by mixtures of processed and transported polluted plumes from the plains. This is the first study that identifies key aerosol populations in the central Indian Himalayas based on in situ measurements and the results are highly important for aerosol-type inventories, chemical transport models and reducing the uncertainty in aerosol radiative forcing over the third pole. (C) 2020 Elsevier B.V. All rights reserved.