It is well reported that the 2015–16 El Niño event is one of the most intense and long lasting events in the 21st century. The quantified changes in the trace gases (Ozone (O₃), Carbon Monoxide (CO) and Water Vapour (WV)) in the tropical upper troposphere and lower stratosphere (UTLS) region are delineated using Aura Microwave Limb Sounder (MLS) and Atmosphere Infrared Radio Sounder (AIRS) satellite observations from June to December 2015. Prior to reaching its peak intensity of El Niño 2015–16, large anomalies in the trace gases (O₃ and CO) were detected in the tropical UTLS region, which is a record high in the 21st century. A strong decrease in the UTLS (at 100 and 82 hPa) ozone (~200 ppbv) in July-August 2015 was noticed over the entire equatorial region followed by large enhancement in the CO (150 ppbv) from September to November 2015. The enhancement in the CO is more prevalent over the South East Asia (SEA) and Western Pacific (WP) regions where large anomalies of WV in the lower stratosphere are observed in December 2015. Dominant positive cold point tropopause temperature (CPT-T) anomalies (~5 K) are also noticed over the SEA and WP regions from the high-resolution Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Global Position System (GPS) Radio Occultation (RO) temperature profiles. These observed anomalies are explained in the light of dynamics and circulation changes during El Niño. Full article

Land use and land cover (LULC) play a crucial role in the interaction between the land and atmosphere, influencing climate at local, regional, and global scales. LULC change due to urbanization has significant impacts on local weather and climate. Land-cover changes associated with urbanization create higher air temperatures compared to the surrounding rural area, known as the “urban heat island (UHI)” effect. Urban landscapes also affect formation of convective storms. In recent years, the effect of urbanization on local convections and lightning has been studied very extensively. In this paper a long-term study has been carried out taking cloud-to-ground (CG) lightning data (1998–2012) from Tai-Power Company, and particulate matter (PM10), sulfur dioxide (SO₂) data (2003–2012) from the Environmental Protection Administration (EPA) of Taiwan, in order to investigate the influence of LULC change through urbanization on CG lightning activity over Taipei taking into account in situ data of population growth, land use change and mean surface temperature (1965–2010). The thermal band of the Land-Sat 7 satellite was used to generate the apparent surface temperature of New Taipei City. It was observed that an enhancement of 60–70% in the flash density over the urban areas compared to their surroundings. The spatial distribution of the CG lightning flashes follows closely the shape of the Taipei city heat island, thereby supporting the thermal hypothesis. The PM10 and SO₂ concentrations showed a positive linear correlation with the number of cloud-to-ground flashes, supporting the aerosol hypothesis. These results indicate that both hypotheses should be considered to explain the CG lightning enhancements over the urban areas. The results obtained are significant and interesting and have been explained from the thermodynamic point of view. Full article

Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R² = 0.80, RMSE = 2.66 gC/m²/day and R² = 0.53, RMSE = 2.12 gC/m²/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (ε_max or LUE_max), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models. Full article

Hanoi City of Vietnam changes quickly, especially after its state implemented its Master Plan 2030 for the city’s sustainable development in 2011. Then, a number of environmental issues are brought up in response to the master plan’s implementation. Among the issues, the Urban Heat Island (UHI) effect that tends to cause negative impacts on people’s heath becomes one major problem for exploitation to seek for mitigation solutions. In this paper, we investigate the land surface thermal signatures among different land-use types in Hanoi. The surface UHI (SUHI) that characterizes the consequences of the UHI effect is also studied and quantified. Note that our SUHI is defined as the magnitude of temperature differentials between any two land-use types (a more general way than that typically proposed in the literature), including urban and suburban. Relationships between main land-use types in terms of composition, percentage coverage, surface temperature, and SUHI in inner Hanoi in the recent two years 2016 and 2017, were proposed and examined. High correlations were found between the percentage coverage of the land-use types and the land surface temperature (LST). Then, a regression model for estimating the intensity of SUHI from the Landsat 8 imagery was derived, through analyzing the correlation between land-use composition and LST for the year 2017. The model was validated successfully for the prediction of the SUHI for another hot day in 2016. For example, the transformation of a chosen area of 161 ha (1.61 km²) from vegetation to built-up between two years, 2016 and 2017, can result in enhanced thermal contrast by 3.3 °C. The function of the vegetation to lower the LST in a hot environment is evident. The results of this study suggest that the newly developed model provides an opportunity for urban planners and designers to develop measures for adjusting the LST, and for mitigating the consequent effects of UHIs by managing the land use composition and percentage coverage of the individual land-use type. Full article

The Inner Mongolia Autonomous Region (IMAR) is a major source of rivers, catchment areas, and ecological barriers in the northeast of China, related to the nation’s ecological security and improvement of the ecological environment. Therefore, studying the response of vegetation to climate change has become an important part of current global change research. Since existing studies lack detailed descriptions of the response of vegetation to different climatic factors using the method of grey correlation analysis based on pixel, the temporal and spatial patterns and trends of enhanced vegetation index (EVI) are analyzed in the growing season in IMAR from 2000 to 2015 based on moderate resolution imaging spectroradiometer (MODIS) EVI data. Combined with the data of air temperature, relative humidity, and precipitation in the study area, the grey relational analysis (GRA) method is used to study the time lag of EVI to climate change, and the study area is finally zoned into different parts according to the driving climatic factors for EVI on the basis of lag analysis. The driving zones quantitatively show the characteristics of temporal and spatial differences in response to different climatic factors for EVI. The results show that: (1) The value of EVI generally features in spatial distribution, increasing from the west to the east and the south to the north. The rate of change is 0.22/10°E from the west to the east, 0.28/10°N from the south to the north; (2) During 2000–2015, the EVI in IMAR showed a slightly upward trend with a growth rate of 0.021/10a. Among them, the areas with slight and significant improvement accounted for 21.1% and 7.5% of the total area respectively, ones with slight and significant degradation being 24.6% and 4.3%; (3) The time lag analysis of climatic factors for EVI indicates that vegetation growth in the study area lags behind air temperature by 1–2 months, relative humidity by 1–2 months, and precipitation by one month respectively; (4) During the growing season, the EVI of precipitation driving zone (21.8%) in IMAR is much larger than that in the air temperature driving zone (8%) and the relative humidity driving zone (11.6%). The growth of vegetation in IMAR generally has the closest relationship with precipitation. The growth of vegetation does not depend on the change of a single climatic factor. Instead, it is the result of the combined action of multiple climatic factors and human activities. Full article

Leaf area index (LAI) is a key input for many land surface models, ecological models, and yield prediction models. In order to make the model simulation and/or prediction more reliable and applicable, it is crucial to know the characteristics and uncertainties of remotely sensed LAI products before they are input into models. In this study, we conducted a comparison of four global remotely sensed LAI products—Global Land Surface Satellite (GLASS), Global LAI Product of Beijing Normal University (GLOBALBNU), Global LAI Map of Chinese Academy of Sciences (GLOBMAP), and Moderate-resolution Imaging Spectrometer (MODIS) LAI, while the former three products are newly developed by three Chinese research groups on the basis of the MODIS land reflectance product over China between 2001 and 2011. Direct validation by comparing the four products to ground LAI observations both globally and over China demonstrates that GLASS LAI shows the best performance, with R² = 0.70 and RMSE = 0.96 globally and R² = 0.94 and RMSE = 0.61 over China; MODIS performs worst (R² = 0.55, RMSE = 1.23 globally and R² = 0.03, RMSE = 2.12 over China), and GLOBALBNU and GLOBMAP performs moderately. Comparison of the four products shows that they are generally consistent with each other, giving the smallest spatial correlation coefficient of 0.7 and the relative standard deviation around the order of 0.3. Compared with MODIS LAI, GLOBALBNU LAI is the most similar, followed by GLASS LAI and GLOBMAP. Large differences mainly occur in southern regions of China. LAI difference analysis indicates that evergreen needleleaf forest (ENF), woody savannas (SAV) biome types and temperate dry hot summer, temperate warm summer dry winter and temperate hot summer no dry season climate types correspond to high standard deviation, while ENF and grassland (GRA) biome types and temperate warm summer dry winter and cold dry winter warm summer climate types are responsible for the large relative standard deviation of the four products. Our results indicate that although the three newly developed products have improved the accuracy of LAI estimates, much work remains to improve the LAI products especially in ENF, SAV, and GRA regions and temperate climate zones. Findings from our study can provide guidance to communities regarding the performance of different LAI products over mainland China. Full article

To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from sequential change patterns of land-covers is first generated, and a rule-based class label image is then prepared for a given time. After the most informative pixels with high uncertainty are selected from the initial classification, rule-based class labels are assigned to the selected pixels. These newly labeled pixels are added to training data, which then undergo an iterative classification process until a stopping criterion is reached. Time-series MODIS NDVI data sets and cropland data layers (CDLs) from the past five years are used for the classification of various crop types in Kansas. From the experiment results, it is found that once the rule-based labels are derived from past CDLs, the labeled informative pixels could be properly defined without analyst intervention. Regardless of different combinations of past CDLs, adding these labeled informative pixels to training data increased classification accuracy and the maximum improvement of 8.34 percentage points in overall accuracy was achieved when using three CDLs, compared to the initial classification result using a small amount of training data. Using more than three consecutive CDLs showed slightly better classification accuracy than when using two CDLs (minimum and maximum increases were 1.56 and 2.82 percentage points, respectively). From a practical viewpoint, using three or four CDLs was the best choice for this study area. Based on these experiment results, the presented approach could be applied effectively to areas with insufficient training data but access to past land-cover maps. However, further consideration should be given to select the optimal number of past land-cover maps and reduce the impact of errors of rule-based labels. Full article

The TROpospheric WAter RAdiometer (TROWARA) is a ground-based microwave radiometer with an additional infrared channel observing atmospheric water parameters in Bern, Switzerland. TROWARA measures with nearly all-weather capability during day- and nighttime with a high temporal resolution (about 10 s). Using the almost complete data set from 2004 to 2016, we derive and discuss the diurnal cycles in cloud fraction (CF), integrated liquid water (ILW) and integrated water vapour (IWV) for different seasons and the annual mean. The amplitude of the mean diurnal cycle in IWV is 0.41 kg/m ${}^2$ . The sub-daily minimum of IWV is at 10:00 LT while the maximum of IWV occurs at 19:00 LT. The relative amplitudes of the diurnal cycle in ILW are up to 25% in October, November and January, which is possibly related to a breaking up of the cloud layer at 10:00 LT. The minimum of ILW occurs at 12:00 LT, which is due to cloud solar absorption. In case of cloud fraction of liquid water clouds, maximal values of +10% are reached at 07:00 LT and then a decrease starts towards the minimum of −10%, which is reached at 16:00 LT in autumn. This breakup of cloud layers in the late morning and early afternoon hours seems to be typical for the weather in Bern in autumn. Finally, the diurnal cycle in rain fraction is analysed, which shows an increase of a few percent in the late afternoon hours during summer. Full article

Snow albedo feedback is one of the most crucial feedback processes that control equilibrium climate sensitivity, which is a central parameter for better prediction of future climate change. However, persistent large discrepancies and uncertainties are found in snow albedo feedback estimations. Remotely sensed snow cover products, atmospheric reanalysis data and radiative kernel data are used in this study to quantify snow albedo radiative forcing and its feedback on both hemispheric and global scales during 2003–2016. The strongest snow albedo radiative forcing is located north of 30°N, apart from Antarctica. In general, it has large monthly variation and peaks in spring. Snow albedo feedback is estimated to be 0.18 ± 0.08 W∙m⁻²∙°C⁻¹ and 0.04 ± 0.02 W∙m⁻²∙°C⁻¹ on hemispheric and global scales, respectively. Compared to previous studies, this paper focuses specifically on quantifying snow albedo feedback and demonstrates three improvements: (1) used high spatial and temporal resolution satellite-based snow cover data to determine the areas of snow albedo radiative forcing and feedback; (2) provided detailed information for model parameterization by using the results from (1), together with accurate description of snow cover change and constrained snow albedo and snow-free albedo data; and (3) effectively reduced the uncertainty of snow albedo feedback and increased its confidence level through the block bootstrap test. Our results of snow albedo feedback agreed well with other partially observation-based studies and indicate that the 25 Coupled Model Intercomparison Project Phase 5 (CMIP5) models might have overestimated the snow albedo feedback, largely due to the overestimation of surface albedo change between snow-covered and snow-free surface in these models. Full article

An optimal payload selection conducted in the frame of the H2020 ONION project (id 687490) is presented based on the ability to cover the observation needs of the Copernicus system in the time period 2020–2030. Payload selection is constrained by the variables that can be measured, the power consumption, and weight of the instrument, and the required accuracy and spatial resolution (horizontal or vertical). It involved 20 measurements with observation gaps according to the user requirements that were detected in the top 10 use cases in the scope of Copernicus space infrastructure, 9 potential applied technologies, and 39 available commercial platforms. Additional Earth Observation (EO) infrastructures are proposed to reduce measurements gaps, based on a weighting system that assigned high relevance for measurements associated to Marine for Weather Forecast over Polar Regions. This study concludes with a rank and mapping of the potential technologies and the suitable commercial platforms to cover most of the requirements of the top ten use cases, analyzing the Marine for Weather Forecast, Sea Ice Monitoring, Fishing Pressure, and Agriculture and Forestry: Hydric stress as the priority use cases. Full article

This work was developed as part of the European H2020 ONION (Operational Network of Individual Observation Nodes) project, aiming at identifying the technological opportunity areas to complement the Copernicus space infrastructure in the horizon 2020–2030 for polar region monitoring. The European Earth Observation (EO) infrastructure is assessed through of comprehensive end-user need and data gap analysis. This review was based on the top 10 use cases, identifying 20 measurements with gaps and 13 potential EO technologies to cover the identified gaps. It was found that the top priority is the observation of polar regions to support sustainable and safe commercial activities and the preservation of the environment. Additionally, an analysis of the technological limitations based on measurement requirements was performed. Finally, this analysis was used for the basis of the architecture design of a potential polar mission. Full article