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Does a warming world, where extremely hot summers are becoming more common, mean that cold summers will never again occur? It is crucial to know whether extremely cold summers are still possible, as such knowledge will significantly impact decisions regarding the further adaptation of crops to cold summers. Japan, which has suffered from many extremely cold summers, has managed past agricultural disruptions with emergency rice imports. In this paper, we show that a climate regime shift associated with the positive phase shift of the summer Arctic Oscillation occurred in 2010 in northeast Eurasia, making the occurrence of extremely cold summers highly unlikely as long as this new regime persists. In fact, Japan has not experienced a cold summer since 2010, while extremely hot summers have been frequent. Since 2010, a double-jet structure with subtropical and polar jets has strengthened, and the polar jet has meandered farther north of Japan, resulting in an upper-tropospheric anticyclone. This anticyclone, which extends downward and tilts southward, reaches southern Japan and prevents cold advection of oceanic air over the cold Oyashio. The Okhotsk high, known as the leading cause of cold summers, has occurred frequently in recent years; however, cold summers have not occurred due to the tilting anticyclone. The recent warming of the Oyashio weakens cold advection. The Pacific–Japan pattern, known as a remote tropical influence, has been weakened. A better understanding of the regime shift will help us understand the tilting anticyclone and the associated extreme summers in northeast Eurasia. Significance Statement: Extremely cold summers are among the most destructive natural disasters, both socioeconomically and agriculturally. Historically, food shortages due to cold summers have triggered wars. This paper proposes that a hemispheric-scale climate regime shift occurred in or around 2010. This regime shift has included warmings in the North Pacific and East Eurasian land surface temperatures. The regime shift is accompanied by the positive shift of the Arctic Oscillation (AO), a jet meander, and an upper-tropospheric anticyclone, making eastern Eurasia extremely hot. Our results imply that extremely cold summers are unlikely to occur in eastern Eurasia so long as this regime persists. Moving forward, it is important that the link between this regime shift and global warming be explored. [ABSTRACT FROM AUTHOR]

As one of the best indicators of the periglacial environment, ice-wedge polygons (IWPs) are important for arctic landscapes, hydrology, engineering, and ecosystems. Thus, a better understanding of the spatiotemporal dynamics and evolution of IWPs is key to evaluating the hydrothermal state and carbon budgets of the arctic permafrost environment. In this paper, the dynamics of ground surface deformation (GSD) in IWP zones (2018–2019) and their influencing factors over the last 20 years in Saskylakh, northwestern Yakutia, Russia were investigated using the Interferometric Synthetic Aperture Radar (InSAR) and Google Earth Engine (GEE). The results show an annual ground surface deformation rate (AGSDR) in Saskylakh at −49.73 to 45.97 mm/a during the period from 1 June 2018 to 3 May 2019. All the selected GSD regions indicate that the relationship between GSD and land surface temperature (LST) is positive (upheaving) for regions with larger AGSDR, and negative (subsidence) for regions with lower AGSDR. The most drastic deformation was observed at the Aeroport regions with GSDs rates of −37.06 mm/a at tower and 35.45 mm/a at runway. The GSDs are negatively correlated with the LST of most low-centered polygons (LCPs) and high-centered polygons (HCPs). Specifically, the higher the vegetation cover, the higher the LST and the thicker the active layer. An evident permafrost degradation has been observed in Saskylakh as reflected in higher ground temperatures, lusher vegetation, greater active layer thickness, and fluctuant numbers and areal extents of thermokarst lakes and ponds. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) is an important variable for controlling the energy exchange between earth's surface and atmosphere. Accurate estimation of the distribution of SSM and its dynamic changes is increasingly essential in the study of hydrology, ecology, agriculture, geological hazard monitoring, and global climate change. This paper presents a new model for SSM retrieval (named, KSSM) through assimilating soil porosity with a two-dimensional feature space composed of land surface temperature and vegetation index (LST-VI). The KSSM model takes into account the physical properties of soil porosity, so it is suitable for SSM retrieval of various soil types in different regions. The KSSM model was validated using the measured data, and a comparison analysis with other data was conducted. Through the sensitivity analysis, it is concluded that the error of SSM retrieved by the KSSM model is less than 0.04 m3 m−3 and the KSSM model is robust. High accuracy of volumetric SSM estimation, error within 10% compared with the measured data, can be achieved by the KSSM model without the need for an in-situ SSM. [ABSTRACT FROM AUTHOR]

This paper illustrates the use of GIS techniques and satellite data in order to analyze the impact of land use change on the local urban microclimate. Specifically, a case study is presented that concerns the city of Vienna. Thereby, satellite-based images were used to classify the city of Vienna into four zones toward the computation of land surface temperatures in two reference years. The classified maps were then statistically projected into the future, resulting in predicted land surface temperatures. The findings highlight the relationship between urbanization and temperature rise in the urban context. The study used data from Landsat 8 satellite in 2013 and 2020. Land cover maps were generated with QGIS for past and current conditions and future land cover maps were projected and corresponding land surface temperatures were predicted. The analysis of satellite data highlighted land surface temperature increase in the city of Vienna. This rise in land surface temperatures correlates with urbanization-driven change in land use and land cover. [ABSTRACT FROM AUTHOR]

Vegetation phenology and its spatiotemporal driving factors are essential to reflect global climate change, the surface carbon cycle and regional ecology, and further quantitative studies on spatiotemporal heterogeneity and its two-way driving are needed. Based on MODIS phenology, meteorology, land cover and other data from 2001 to 2019, this paper analyzes the phenology change characteristics of the Yangtze River Delta from three dimensions: time, plane space and elevation. Then, the spatiotemporal heterogeneity of phenology and its driving factors are explored with random forest and geographic detector methods. The results show that (1) the advance of start of season (SOS) is insignificant—with 0.17 days per year; the end of season (EOS) shows a significant delay—0.48 days per year. The preseason temperature has a greater contribution to SOS, while preseason precipitation is main factor in determining EOS. (2) Spatial differences of the phenological index do not strictly obey the change rules of latitude at a provincial scale. The SOS of Jiangsu and Anhui is earlier than that of Zhejiang and Shanghai, and EOS shows an obvious double-clustering phenomenon. In addition, a divergent response of EOS with elevation grades is found; the most significant changes are observed at grades below 100 m. (3) Land cover (LC) type is a major factor of the spatial heterogeneity of phenology, and its change may also be one of the insignificant factors driving the interannual change of phenology. Furthermore, nighttime land surface temperature (NLST) has a relatively larger contribution to the spatial heterogeneity in non-core urban areas, but population density (PD) contributes little. These findings could provide a new perspective on phenology and its complex interactions between natural or anthropogenic factors. [ABSTRACT FROM AUTHOR]

Solar energy has developed rapidly in recent years, and its impacts on climate have been revealed mainly through model simulations and limited field observations. However, the representation of solar plants in these models is highly simplistic and does not account for dynamical effects, which may pose risks under the simulations for tracking photovoltaics (PV). To make a more comprehensive assessment of the PV-induced climatic impacts, it is relevant to further develop a more sophisticated PV module based on the observed PV-induced impacts from specific cases. To remedy the deficiencies in the existing PV energy models, the newly sophisticated PV module established in this paper includes both the land surface radiation balance, sensible heat balance and the surface physical dray process over the locations of PV plants. It is then used to simulate the local climatic impacts of PV plants through three parallel sensitivity experiments. Comparison analysis between control run and two PV installation scenarios showed obvious changes in the 10-m wind speed, land surface temperature, and 2-m specific humidity were found locally over the locations of PV plants during both the warm and cold periods. The magnitudes of these changes are positively related to the installed capacity of the PV plants, but are much smaller than the natural climate variations. It is expected that the observation-based PV module established in this paper is a meaningful attempt for the model simulation on this field. • A newly sophisticated PV model is firstly developed based on observations. • PV-induced reduction of 10-m wind speed is ∼0.7–1.0 m/s. • Land surface temperature decreases by ∼ 1–2 K during daytime. • PV-induced drying change is obvious in January (∼0.1 kg/kg) but not in July. • The local climatic impacts are much smaller than the natural climate variation. [ABSTRACT FROM AUTHOR]

Observations of warm past climates and projections of future climate change show that the Arctic warms more than the global mean, particularly during winter months. Previous work has attributed this reduced Arctic land seasonality to the effects of sea ice or clouds. In this paper, we show that the reduced Arctic land seasonality is a robust consequence of the relatively small surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission, without recourse to other processes or feedbacks. We use a general circulation model (GCM) with no clouds or sea ice and a simple representation of land. In the annual mean, the equator-to-pole surface temperature gradient falls with increasing CO2, but this is only a near-surface phenomenon and is not caused by the change in total meridional heat transport, which is virtually unaltered. The high-latitude land has about twice as much warming in winter than in summer, whereas high-latitude ocean has very little seasonality in warming. A surface energy balance model shows how the combination of the smaller surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission gives rise to the reduced seasonality of the land surface. The increase in evaporation over land also leads to winter amplification of warming over land, although amplification still occurs without it. While changes in clouds, sea ice, and ocean heat transport undoubtedly play a role in high-latitude warming, these results show that enhanced land surface temperature warming in winter can happen in their absence for robust reasons. [ABSTRACT FROM AUTHOR]

Accurate estimations of daily mean land surface temperature (LST) are important for investigating the urban heat island effect, land-atmosphere energy exchanges, and global climate change. Moderate Resolution Imaging Spectroradiometer (MODIS) sensors can provide up to four instantaneous LSTs of a single day across the world. However, numerous studies, such as those on climate change and hydrology, require the input of daily mean LSTs rather than instantaneous value. In this paper, we propose a practical method to estimate the daily mean LST using instantaneous LST products derived from MODIS. Based on the in situ LST measurements collected from 235 sites distributed globally, multiple linear regressions of two to four valid instantaneous LSTs at different MODIS observations moments (at least one daytime and one nighttime observations) can provide reliable estimates of daily mean LSTs under all-weather conditions with a root mean square error (RMSE) of less than 1.60 K. In addition, the conditions of clouds would affect the estimation accuracy of daily mean LST to a certain extent. Subsequently, an algorithm is proposed to produce the most complete coverage of daily mean LSTs from instantaneous LST products derived from MODIS. Validation results with in situ measurements show that the daily mean LSTs estimated from the MOD11A1 and MYD11A1 products are similar to the daily mean of the in situ LST, with an RMSE of 2.17 K. Furthermore, the daily mean LST derived from MODIS data is successfully applied to calculate the global annual cycle parameters (ACPs) in the annual temperature cycle (ATC) model. The results of this study show that the daily mean LST can be retrieved accurately from combinations of daytime and nighttime LSTs derived from MODIS. We expect that our findings will be useful for various applications involving global LST trend analysis and climate change. [ABSTRACT FROM AUTHOR]

The Middle East is an area characterized by strong gradients in meteorological conditions and varied land cover. As a result, some of the hottest places in the world as well as very cold areas can be found in this region. The objective of this paper is to quantitatively assess land surface temperature (LST) variations in the countries of the Middle East to estimate the trends and determine whether relationships suggesting the impact of climate change can be observed. Spatiotemporal variability of LST was investigated through the analysis of MODIS Terra LST images (MOD11A2) from 2001 to 2018. The LST differences in both daytime series and seasonal and international means were assessed. The analyses showed that in spring (MAM), about 20,000 km2 of the study area has an LST higher than 50 °C, whereas 3700 km2 has LST lower than 0 °C. In summer (JJA), about 1 million km2 has LST higher than 50 °C. In the fall (SON), about 5 million km2 of the study area has a land surface temperature higher than 30 °C, with about 11 km2 hotter than 50 °C, whereas 320 km2 has LST lower than 0 °C. In winter (DJF), the hottest countries are Yemen, Oman, and Saudi Arabia, with 44.7, 41.8, and 39.8 °C, respectively, whereas the lowest LST values were recorded in Turkey, Iran, and Iraq, with −19.5, −18.1, and −10.5 °C, respectively. An upward trend in the minimum and a downward trend in the maximum value of the winter LST suggest that winters in the Middle East countries such as Iran, Israel, and Jordan have become milder during the considered period. Negative trends in the spring LST in Bahrain and Oman and the summer LST in Bahrain and Qatar suggest that these seasons in those countries became colder during the study period. [ABSTRACT FROM AUTHOR]

Land Surface Temperature refers back to the calculation of ground temperature that is made with extraordinary factors. Due to changes in the land surface temperature of the area, the climate and vegetation are also affected. The Normalized Difference Vegetation Index (NDVI) serves as an indicator of vegetation abundance to estimate the land surface temperature (LST) vegetation relationship. Landsat 7 ETM+ and Landsat 8 OLI TIRS images for four years (2005, 2010, 2015, and 2020) are used to study the LST changes in the research area (Jammu & Kashmir). Correlations between NDVI and LST are calculated. Through the study, it is clear that the Normalized Difference Vegetation Index plays a major role in the calculation of land surface temperature. In this study, the change in the higher temperature noted in the year 2005 was 20oC, and in the year 2020, the highest temperature was 25oC. If we talk about the lower temperature, the change is 1oC. Both Jammu and Kashmir have semideciduous vegetation that includes subtropical pine forests. [ABSTRACT FROM AUTHOR]

Urban growth is a worldwide phenomenon. The rate of urbanisation in developing countries such as Algeria is speedy. Sub-watershed of Oued Fekan is included in the large watershed of Macta which is located in northwestern Algeria and is one of the most important sites of this country characterized by an abundant amount of biodiversity as well as a highly productive ecosystem. The valuable landscape undergoes a radical change in the form of a sub-watershed recently due to anthropogenic change on land use and land cover. The exponential increase in population and human activities are increasing the demand for land and soil resources for agriculture, urban and industrial uses. Anthropogenic factors, especially urban sprawl, have a significant role in controlling the temperature change. In this paper, four Landsat-8 OLI/TIRS images of 2018 have been used from different seasons to estimate land surface temperature (LST), Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) in order to study the phenomenon of difference distribution temperature in urban with the surrounding rural areas. Analysis based on linear regression was used to generate relationships between LST with NDVI and NDBI. Our analysis indicates that for the four seasons, a strong linear relationship between NDBI and LST was marked compared with the relationship between NDVI and LST, which was less intense and varied by seasons. We suggest that NDBI is a visible indicator for studying surface Urban Heat Island phenomenon (UHI). Useful information that occurs as a consequence of land-use changes and urbanization are then provided for understanding the local climate and environmental changes of our study area. [ABSTRACT FROM AUTHOR]

• A comprehensive review of remote sensing applications in water resources monitoring and management in Africa is provided. • Evolution of remote sensing techniques, particularly in hydrological modeling is summarized. • Role of sensors, observations, and analytical models in monitoring Africa's water resources at different scales are highlighted. • In-situ hydrological monitoring networks and limited long-term rainfall and climate data remains a challenge in Africa. • The adoption of multi-source data integration, and big data analytics provides new opportunities to improve water resource management in Africa. This paper provides an overview of the progress made in remote sensing of water resources in Africa, focusing on various applications such as precipitation estimation, land surface temperature analysis, soil moisture assessment, surface water extent measurement, surface runoff and streamflow analysis, water quality evaluation, land cover/land use mapping, and groundwater analysis. Specifically, the study sheds light on the remarkable progress made in remote sensing applications, showcasing scientific advancements and highlighting the challenges encountered. Moreover, it identifies crucial knowledge gaps that need to be addressed in order to further advance this field. The review's key findings indicate that the availability of sensors and observations, along with analytical models, has contributed to monitoring Africa's water resources at various scales. The availability and accessibility of hydrological data for monitoring and assessing water resources in Africa have been partially improved through the adoption of satellite data and processing technologies. Additionally, the emergence of various international remote sensing initiatives, North-South research collaborations, and projects has contributed to the research progress. Prominent satellite data series such as Landsat, MODIS, and GRACE have played significant roles in African hydrological research. However, the limited and malfunctioning in-situ hydrological monitoring networks in Africa have affected the accurate calibration and validation of remotely sensed hydrological models. Insufficient long-term rainfall and climate data pose challenges to long-term earth observation research on African water systems. The lack of high-resolution spatial and temporal data hampered accurate monitoring of hydrological processes at smaller scales. Despite the widespread use of rainfall satellite products, validation attempts over Africa, particularly in western and southern regions, have been limited. Furthermore, the reliability of hydrological satellite datasets is affected by factors such as the number and coverage of surface stations, retrieval algorithms, data integration techniques, and cloud cover. Overall, this work demonstrates the importance of earth observation in understanding Africa's hydrology, previously hindered by the lack of in-situ data. Nevertheless, efforts are therefore needed to enhance the adoption and application of remote sensing, particularly in groundwater and surface water estimation at smaller scales. Future research should focus on multi-source data integration, assimilation, and big data analytics using cloud computing and machine learning to address complex hydrological research questions at various scales. [ABSTRACT FROM AUTHOR]

The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software called YAO was used as a framework to implement a 4D-VAR assimilation scheme of observations in SECHIBA. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obtained with YAO to provide an opportunity for scientists and end users to perform their own assimilation. SECHIBA-YAO allows the control of the 11 most influential internal parameters of the soil water content, by observing the land surface temperature or remote sensing data such as the brightness temperature. The paper presents the fundamental principles of the 4D-VAR assimilation, the semi-generator software YAO and a large number of experiments showing the accuracy of the adjoint code in different conditions (sites, PFTs, seasons). In addition, a distributed version is available in the case for which only the land surface temperature is observed. [ABSTRACT FROM AUTHOR]

The Earth's climate is largely determined by its energy budget. Since the 1960s, satellite remote sensing has been used in estimating these energy budget components at both the top of the atmosphere (TOA) and the surface. Besides the broadband sensors that have been traditionally used for monitoring Earth's Energy Budget (EEB), data from a variety of narrowband sensors aboard both polar-orbiting and geostationary satellites have also been extensively employed to estimate the EEB components. This paper provides a comprehensive review of the satellite missions, state-of-the art estimation algorithms and the satellite products, and also synthesizes current understanding of the EEB and spatio-temporal variations. The TOA components include total solar irradiance, reflected shortwave radiation/planetary albedo, outgoing longwave radiation, and energy imbalance. The surface components include incident solar radiation, shortwave albedo, shortwave net radiation, longwave downward and upwelling radiation, land and sea surface temperature, surface emissivity, all-wave net radiation, and sensible and latent heat fluxes. Some challenges, and outlook such as virtual constellation of different satellite sensors, temporal homogeneity tests of long time-series products, algorithms ensemble, and products intercomparison are also discussed. [ABSTRACT FROM AUTHOR]

Rapid urbanization leads to an increase of the LST and is an important index in global climate change. The aim of this research are to study the effect of vegetation and urbanization over land surface temperature of Iaşi city, România, using remote sensing (RS) data. Landsat 8 OLI data were used to evaluate LST and the deduction of urban indices (NDBI-normalized difference built-up index, UI-urban index) and vegetation (NDVINormalized difference vegetation index and SAVI-soil adjusted vegetation index) indices. On May and August 2016 images was taken, when the vegetation was in full season and temperatures are high in Iaşi. From that paper, it has been observed that the zones with high LST were placed especially in central built-up zones. The assessment of relationships between LST with SAVI, NDVI, NDBI and UI shows that LST have the negative correlation with vegetation indices (NDVI and SAVI) and positive correlation with urban vegetation (NDBI and UI) that means that green land (green spaces) could attenuate the UHI effect whereas built-up areas can consolidate the effect of UHI. It has been deducted that vegetation (NDVI, SAVI) and urban indices (NDBI, UI) could be used to investigate the risk of UHI and might help city planners better train for possible impacts of urban environmental change. [ABSTRACT FROM AUTHOR]

Abstract The phenomenon of increasing the temperature of the Earth's atmosphere over the years is known as the Global warming. The Global warming has several adverse effects on our climate, our health and our communities. The resulting climate changes due to global warming produce significant changes in the weather patterns. It is also argued that the climate changes occurring due to global warming is responsible for the various geological hazards occurring in the world, like earthquakes, volcanic eruptions, tsunamis (Gutenberg and Richter, 1954; Turcotte and Schubert, 2002; Wu and Johnston, 2000; Wu and Hasegawa, 1996a; Wu and Hasegawa, 1996b; Wu, 1997). McGuire in his book (McGuire, 2012) has explained in detail the interlink between the climate changes occurring due to global warming and the occurrence of geological disasters like earthquakes, volcanoes and tsunamis. Looking back to the past, when the climate changed naturally and our planet emerged from the ice age, the huge pressure exerted on the Earth's crust suddenly disappeared causing the Earth's crust to bounce back, the increased stress on the Earth's crust activated earthquakes, tremors and volcanic eruptions along the pre-existing fault lines. Currently, our planet, the Earth is still responding to the end of the last ice age some 20,000 or more years ago when the temperatures began to rise, huge masses of ice-sheets disappeared resulting from the melting of large masses of ice. Thus the rising temperature may be related to the anomalous emanation of soil radon-222 gas prior to an earthquake. The prime focus of this paper is to search for any cross correlation existing between the daily land-surface temperature anomalies and the soil radon concentration using a robust state of art method, Multifractal detrended cross correlation analysis (MF-DXA). The data sets for both the series used for the analysis are for a period from 2005 to 2014. The analysis shows that these two time series are not only cross correlated but having the cross correlation coefficient γ X = 0. 012 ± 0. 01 , which clearly indicates a strong correlation between the soil radon-222 anomalies and the daily land-surface temperature anomalies. Highlights • Cross correlation between land-surface temperature anomaly and soil radon emission. • Multifractal detrended cross correlation analysis (MF-DXA) has been applied. • The value of the cross correlation coefficient (γ X) is γ X = 0. 012 ± 0. 01. • The positive low value of γ X clearly confirms the strong cross correlation. • The value of the spectral width ( W X) suggests the multifractal nature of cross correlation. [ABSTRACT FROM AUTHOR]

Climate change conditions a wide range of impacts such as the impact on weather, but also on ecosystems and biodiversity, agriculture and forestry, human health, hydrological regime and energy. In addition to global warming, local factors affecting climate change are being considered. Presentation and analysis of the situation was carried out using geoinformation technologies (radar recording, remote detection, digital terrain modeling, cartographic visualization and geostatistics). This paper describes methods and use of statistical indicators such as LST, NDVI and linear correlations from which it can be concluded that accelerated construction and global warming had an impact on climate change in period from 1987 to 2018 in the area of Vojvodina - Republic of Serbia. Also, using the global SRTM DEM, it is shown how the temperature behaves based on altitude change. Conclusions and possible consequences in nature and society were derived. [ABSTRACT FROM AUTHOR]

Abstract Soil moisture (SM) is a key state variable in understanding the climate system through its control on the land surface energy, water budget partitioning, and the carbon cycle. Monitoring SM at regional scale has become possible thanks to microwave remote sensing. In the past two decades, several satellites were launched carrying on board either radiometer (passive) or radar (active) or both sensors in different frequency bands with various spatial and temporal resolutions. Soil moisture algorithms are in rapid development and their improvements/revisions are ongoing. The latest SM retrieval products and versions of products that have been recently released are not yet, to our knowledge, comprehensively evaluated and inter-compared over different ecoregions and climate conditions. The aim of this paper is to comprehensively evaluate the most recent microwave-based SM retrieval products available from NASA's (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) satellite, ESA's led mission (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) satellite, ASCAT (Advanced Scatterometer) sensor on board the meteorological operational (Metop) platforms Metop-A and Metop-B, and the ESA Climate Change Initiative (CCI) blended long-term SM time series. More specifically, in this study we compared SMAPL3 V4, SMOSL3 V300, SMOSL2 V650, ASCAT H111, and CCI V04.2 and the new SMOS-IC (V105) SM product. This evaluation was achieved using four statistical scores: Pearson correlation (considering both original observations and anomalies), RMSE, unbiased RMSE, and Bias between remotely-sensed SM retrievals and ground-based measurements from >1000 stations from 17 monitoring networks, spread over the globe, disseminated through the International Soil Moisture Network (ISMN). The analysis reveals that the performance of the remotely-sensed SM retrievals generally varies depending on ecoregions, land cover types, climate conditions, and between the monitoring networks. It also reveals that temporal sampling of the data, the frequency of data in time and the spatial coverage, affect the performance metrics. Overall, the performance of SMAP and SMOS-IC products compared slightly better with respect to the ISMN in situ observations than the other remotely-sensed products. Highlights • Performances of SMOS, SMAP, CCI, and ASCAT soil moisture products were evaluated against ISMN datasets. • High performances of all products were found over Africa and Australia. • Performances of SMOS, SMAP, CCI, and ASCAT are complementary depending on the evaluation protocol, climate and vegetation. • SMAP and SMOS-IC follow slightly better the temporal dynamics of in situ observations. [ABSTRACT FROM AUTHOR]

Nowadays, a multitude of world changes are coming to affect the ecosystems, urban landscapes, and natural resources. Land use and land cover patterns represent an interactive layer between Earth surface and underground entities which is often consider in the hydrogeological studies. Many factors such as climate parameters and human activities affects the groundwater recharge, quality of water resources and also changes in the hydrologic regime. Crop evapotranspiration plays an essential role in the water balance and water deficit investigations. Here, we use the seasonal crop coefficients to calibrate the annual crop coefficients for the Italian territory. These values could be useful for the temperate zone area. Four stages of annual crop coefficients were set (initial, mid-season, end season, cold season) to perform the calculation of annual crop coefficients. The annual crop coefficients (AKc) have been extracted from the annual ETc. CORINE land cover data and high resolution climate models of potential evapotranspiration (ET0) were the key datasets for AKc projection in Italy. These findings and original maps that we show in the paper represent important tools for the agriculture, environmental, and urban planning. [ABSTRACT FROM AUTHOR]

Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening (TsHARP) method and a binary linear model are compared to downscale the original daytime FengYun 2F (FY-2F) land surface temperature (LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land (SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error (RMSE) of the LST downscaled from the binary linear model is 1.30°C compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78°C. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37°C in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37°C and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data. [ABSTRACT FROM AUTHOR]

This paper investigates the sensitivity of projected future climate changes over China to the horizontal resolution of a regional climate model RegCM4.4 (RegCM), using RCP8.5 as an example. Model validation shows that RegCM performs better in reproducing the spatial distribution and magnitude of present-day temperature, precipitation and climate extremes than the driving global climate model HadGEM2-ES (HadGEM, at 1.875° × 1.25° degree resolution), but little difference is found between the simulations at 50 and 25 km resolutions. Comparison with observational data at different resolutions confirmed the added value of the RCM and finer model resolutions in better capturing the probability distribution of precipitation. However, HadGEM and RegCM at both resolutions project a similar pattern of significant future warming during both winter and summer, and a similar pattern of winter precipitation changes including dominant increase in most areas of northern China and little change or decrease in the southern part. Projected precipitation changes in summer diverge among the three models, especially over eastern China, with a general increase in HadGEM, little change in RegCM at 50 km, and a mix of increase and decrease in RegCM at 25 km resolution. Changes of temperature-related extremes (annual total number of daily maximum temperature > 25 °C, the maximum value of daily maximum temperature, the minimum value of daily minimum temperature in the three simulations especially in the two RegCM simulations are very similar to each other; so are the precipitation-related extremes (maximum consecutive dry days, maximum consecutive 5-day precipitation and extremely wet days’ total amount). Overall, results from this study indicate a very low sensitivity of projected changes in this region to model resolution. While fine resolution is critical for capturing the spatial variability of the control climate, it may not be as important for capturing the climate response to homogeneous forcing (in this case greenhouse gas concentration changes). [ABSTRACT FROM AUTHOR]

The earth is experiencing a profound climate change which influences the land surface processes. The application of geospatial techniques helps people to obtain skills for measuring spatiotemporal patterns of climate change and evaluating its impacts on the ecosystem and human life. This teaching module focuses on the basic concepts of climate change and adaptation and employs case studies and interactive exercises to measure the elements of climate change and their spatial distribution. Its high-level goal is to use modelling tools to generate climate change scenarios. The current paper summarizes the learning objectives, lesson content, learning activities and evaluation scheme for this module. [ABSTRACT FROM AUTHOR]

This paper presents the background, scientific objectives, experimental design, and preliminary achievements of the Third Tibetan Plateau (TP) Atmospheric Scientific Experiment (TIPEX-III) for 8-10 years. It began in 2013 and has expanded plateau-scale observation networks by adding observation stations in data-scarce areas; executed integrated observation missions for the land surface, planetary boundary layer, cloud-precipitation, and troposphere-stratosphere exchange processes by coordinating ground-based, air-based, and satellite facilities; and achieved noticeable progress in data applications. A new estimation gives a smaller bulk transfer coefficient of surface sensible heat over the TP, which results in a reduction of the possibly overestimated heat intensity found in previous studies. Summer cloud-precipitation microphysical characteristics and cloud radiative effects over the TP are distinguished from those over the downstream plains. Warm rain processes play important roles in the development of cloud and precipitation over the TP. The lower-tropospheric ozone maximum over the northeastern TP is attributed to the regional photochemistry and long-range ozone transports, and the heterogeneous chemical processes of depleting ozone near the tropopause might not be a dominant mechanism for the summer upper-tropospheric-lower-stratospheric ozone valley over the southeastern TP. The TP thermodynamic function not only affects the local atmospheric water maintenance and the downstream precipitation and haze events but also modifies extratropical atmospheric teleconnections like the Asia-Pacific Oscillation, subtropical anticyclones over the North Pacific and Atlantic, and temperature and precipitation over Africa, Asia, and North America. These findings provide new insights into understanding land-atmosphere coupled processes over the TP and their effects, improving model parameterization schemes, and enhancing weather and climate forecast skills. [ABSTRACT FROM AUTHOR]

Incoming surface solar irradiance (SSI) is essential for calculating Earth's surface radiation budget and is a key parameter for terrestrial ecological modeling and climate change research. Remote sensing images from geostationary and polar-orbiting satellites provide an opportunity for SSI estimation through directly retrieving atmospheric and land-surface parameters. This paper presents a new scheme for estimating SSI from the visible and infrared channels of geostationary meteorological and polar-orbiting satellite data. Aerosol optical thickness and cloud microphysical parameters were retrieved from Geostationary Operational Environmental Satellite (GOES) system images by interpolating lookup tables of clear and cloudy skies, respectively. SSI was estimated using pre-calculated offline lookup tables with different atmospheric input data of clear and cloudy skies. The lookup tables were created via the comprehensive radiative transfer model, Santa Barbara Discrete Ordinate Radiative Transfer (SBDART), to balance computational efficiency and accuracy. The atmospheric attenuation effects considered in our approach were water vapor absorption and aerosol extinction for clear skies, while cloud parameters were the only atmospheric input for cloudy-sky SSI estimation. The approach was validated using one-year pyranometer measurements from seven stations in the SURFRAD (SURFace RADiation budget network). The results of the comparison for 2012 showed that the estimated SSI agreed with ground measurements with correlation coefficients of 0.94, 0.69, and 0.89 with a bias of 26.4 W/m-2, -5.9 W/m-2, and 14.9 W/m-2 for clear-sky, cloudy-sky, and all-sky conditions, respectively. The overall root mean square error (RMSE) of instantaneous SSI was 80.0 W/m-2 (16.8%), 127.6 W/m-2 (55.1%), and 99.5 W/m-2 (25.5%) for clear-sky, cloudy-sky (overcast sky and partly cloudy sky), and all-sky (clear-sky and cloudy-sky) conditions, respectively. A comparison with other state-of-the-art studies suggests that our proposed method can successfully estimate SSI with a maximum improvement of an RMSE of 24 W/m-2. The clear-sky SSI retrieval was sensitive to aerosol optical thickness, which was largely dependent on the diurnal surface reflectance accuracy. Uncertainty in the pre-defined horizontal visibility for 'clearest sky' will eventually lead to considerable SSI retrieval error. Compared to cloud effective radius, the retrieval error of cloud optical thickness was a primary factor that determined the SSI estimation accuracy for cloudy skies. Our proposed method can be used to estimate SSI for clear and one-layer cloud sky, but is not suitable for multi-layer clouds overlap conditions as a lower-level cloud cannot be detected by the optical sensor when a higher-level cloud has a higher optical thickness. [ABSTRACT FROM AUTHOR]

The aim of Copernicus Climate Change Service (C3S) is to supply reliable climate data in support of strategies to adaptation and mitigation to climate change. The C3S provides access to high-quality climate data through its Climate Data Records (CDRs) of atmospheric, marine and land Essential Climate Variables (ECVs). Global Earth Surface Albedo (SA) satellite-based products are included in the land (biosphere) portfolio. SA is a magnitude which quantifies the fraction of solar energy reflected by the surface of the Earth. This paper details the retrieval methodology and preliminary validation results for global estimates of surface albedo based on Sentinel-3 observations for the C3S ECVs data (C3S SA v3.0). The retrieval algorithm exploits the synergistic use of the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR) on-board Sentinel-3 A and B satellites. Firstly, the atmospherically corrected reflectances are generated in the Copernicus Global Land Service framework. After that, the Bidirectional Reflectance Distribution Function (BRDF) inversion module concludes the BRDF model parameters, which are transferred to the angular integration module in order to generate spectral albedo quantities for the selected OLCI (Oa03, Oa04, Oa07, Oa17 and Oa21) and SLSTR (S1, S2, S5 and S6) bands. At the end, the spectral integration module generates broadband albedo quantities in three different standard broadband spectral regions (visible [0.4μ m − 0.7μ m ], near infrared [0.7μ m − 4μ m ] and total shortwave [0.3μ m − 4μ m ]). Preliminary validation results over 10-months demonstration period (July 2018–April 2019) show, in terms of spatial and temporal consistency, that C3S Sentinel-3 SA global estimates reached in general good agreement as compared to other satellite operational references derived from MODIS (MCD43A3 C6) and PROBA-V (C3S PROBA-V SA v1.0) acquisitions. The comparison with ground data shows similar results to the MCD43A3 C6 comparisons but opposite sign in differences (marginally positive in case of Sentinel-3), with accuracy of 0.005 (3.7%), precision of 0.016 (11.3%) and uncertainty of 0.032 (22.7%). Our results have demonstrated the feasibility to estimate global fields of SA from Sentinel-3 observations, with similar quality of existing operational products. These Sentinel-3 based SA datasets will give the continuity to the existing C3S SA CDR, introducing improvements in terms of spatial resolution (300 m) and spectral information (9 spectral albedos) in contrast to previous datasets based on Advanced Very High Resolution Radiometer (AVHRR; 4 km, 4 channels) and Vegetation instruments (VGT; 1 km, 4 channels). • Retrieval method for global surface albedo estimates from Sentinel-3 is proposed. • Algorithm is based on BRDF inversion, angular and spectral integration. • A cross-comparison with MODIS and PROBA-V satellite products is performed. • The direct validation includes comparison with spatially representative ground data. [ABSTRACT FROM AUTHOR]

The 2015 release of the precipitation climatology from the Global Precipitation Climatology Centre (GPCC) for 1951-2000, based on climatological normals of about 75,100 rain gauges, allows for quantification of mean land surface precipitation as part of the global water cycle. In GPCC's 2011-release, a bulk climatological correction was applied to compensate for gauge undercatch. In this paper we derive an improved correction approach based on the synoptic weather reports for the period 1982-2015. The compared results show that the climatological approach tends to overestimate the correction for Central and Eastern Europe, especially in the northern winter, and in other regions throughout the year. Applying the mean weather-dependent correction to the GPCC's uncorrected precipitation climatology for 1951-2000 gives a value of 854.7 mm of precipitation per year (excluding Antarctica) or 790 mm for the global land surface. The warming of nearly 1 K relative to pre-industrial temperatures is expected to be accompanied by a 2%-3% increase in global (land and ocean) precipitation. However, a comparison of climatology for 30-year reference periods from 1931-1960 up to 1981-2010 reveals no significant trend for land surface precipitation. This may be caused by the large variability of precipitation, the varying data coverage over time and other issues related to the sampling of rain-gauge networks. The GPCC continues to enlarge and further improve the quality of its database, and will generate precipitation analyses with homogeneous data coverage over time. Another way to reduce the sampling issues is the combination of rain gauge-based analyses with remote sensing (i.e., satellite or radar) datasets. [ABSTRACT FROM AUTHOR]

Deforestation is expanding and accelerating into the remaining areas of undisturbed forest, and the quality of the remaining forests is declining today. Assessing the climatic impacts of deforestation can help to rectify this alarming situation. In this paper, how historical deforestation may affect global climate through interactive ocean and surface albedo is examined using an Earth system model of intermediate complexity (EMIC). Control and anomaly integrations are performed for 1000 years. In the anomaly case, cropland is significantly expanded since AD 1700. The response of climate in deforested areas is not uniform between the regions. In the background of a global cooling of 0.08 °C occurring with cooler surface air above 0.4 °C across 30° N to 75° N from March to September, the surface albedo increase has a global cooling effect in response to global-scale replacement of forests by cropland, especially over northern mid-high latitudes. The northern mid-latitude (30° N-60° N) suffers a prominent cooling in June, suggesting that this area is most sensitive to cropland expansion through surface albedo. Most regions show a consistent trend between the overall cooling in response to historical deforestation and its resulting cooling due to surface albedo anomaly. Furthermore, the effect of the interactive ocean on shaping the climate response to deforestation is greater than that of prescribed SSTs in most years with a maximum spread of 0.05 °C. This difference is more prominent after year 1800 than that before due to the more marked deforestation. These findings show the importance of the land cover change and the land surface albedo, stressing the necessity to analyze other biogeophysical processes of deforestation using interactive ocean. [ABSTRACT FROM AUTHOR]

Surface upward longwave radiation (SULR) is an important component of the surface energy balance and is closely related to land surface temperature and emissivity. The estimation of SULR plays an important role in the study of surface energy circulation and climate change. State-of-the-art methods to estimate SULR, including the physical method and the hybrid method, are conducted without considering directional thermal radiation (DTR), which may induce a large error in the estimation, particularly over sparsely vegetated surfaces. In this paper, we modified the physical temperature–emissivity algorithm by combining a directional emissivity model (FRA97) and a kernel-driven DTR model to estimate the SULR of vegetated surfaces while considering the thermal directionality of the land surface. The most suitable kernel-driven model and an angle combination of the DTR were selected from six kernel-driven models and five angular combinations. The sensitivity of the proposed algorithm to the input parameters was also analyzed. The proposed algorithm was then validated with the Wide-angle infrared Dual-mode line/area Array Scanner (WiDAS) data set and longwave radiation data of automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results showed that the five-angle combination with large-angle intervals performs the best. When the leaf area index (LAI) is less than 1.2, the RossThick-LiSparseR model performs the best; when LAI is larger than 1.2, the RossThick-LiDenseR model is the most accurate. The SULR is not sensitive to surface downward longwave radiation and LAI, is slightly sensitive to leaf and soil emissivity at certain LAIs, and is highly sensitive to DTR, which may greatly affect the accuracy of the estimated SULR. The root-mean-square error (RMSE) and the mean bias error (MBE) of the SULR estimated using the WiDAS data and the proposed algorithm are 5.618 and −1.642 W/m2, respectively, thereby improving the estimation accuracy by as much as 7.479 and 10.511 W/m2 at most in terms of RMSE and MBE, respectively, compared with the results calculated without considering the DTR. [ABSTRACT FROM PUBLISHER]

The seasonal and interannual dynamics of tropical rainforests play a critical role in the global carbon cycle and climate change. This paper retrieved and compared land surface phenology from observations acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard geostationary satellites and the Moderate Resolution Imaging Spectroradiometer (MODIS) on polar-orbiting satellites over the Congo Basin. To achieve this, we first retrieved canopy greenness cycles (CGCs) and their transition timing from two-band enhanced vegetation index (EVI2) derived from SEVIRI and MODIS data between 2006 and 2013. We then assessed the influences of SEVIRI and MODIS data quality on the reconstruction of the EVI2 temporal trajectory, the detection of the CGC onset and end timing, and the total number of successful CGC retrievals. The significance of influences was determined using the one-tailed two-sample Kolmogorov–Smirnov test. The results indicate that diurnal SEVIRI observations greatly increased the probability of capturing cloud-free daily EVI2 in the rainforest-dominated region of the Congo Basin, where the proportion of good quality (PGQ) observations during a CGC was up to 80% higher than that from MODIS. As a result, the double annual CGCs of the Congo Basin rainforests were well identified from SEVIRI but sparsely detected from MODIS, whereas the single annual CGC in the savanna-dominated northern and southern Congo Basin was successfully retrieved from both SEVIRI and MODIS. Moreover, the decreases of PGQ in an EVI2 time series were found to significantly increase the uncertainties of retrieved phenological timings and increase the probabilities of CGC retrieval failures. [ABSTRACT FROM PUBLISHER]

Urban heat island (UHI), referring to higher temperatures in urban extents than its surrounding rural regions, is widely reported in terms of negative effects to both the ecological environment and human health. To propose effective mitigation measurements, spatiotemporal variations and control machines of surface UHI (SUHI) have been widely investigated, in particular based on the indicator of SUHI intensity (SUHII). However, studies on SUHI frequency (SUHIF), an important temporal indicator, are challenged by a large number of missing data in daily land surface temperature (LST). Whether there is any city with strong SUHII and low SUHIF remains unclear. Thanks to the publication of daily seamless all-weather LST, this paper is proposed to investigate spatiotemporal variations of SUHIF, to compare SUHII and SUHIF, to conduct a pattern classification, and to further explore their driving factors across 305 Chinese cities. Four main findings are summarized below: (1) SUHIF is found to be higher in the south during the day, while it is higher in the north at night. Cities within the latitude from 20° N and 40° N indicate strong intensity and high frequency at day. Climate zone-based variations of SUHII and SUHIF are different, in particular at nighttime. (2) SUHIF are observed in great diurnal and seasonal variations. Summer daytime with 3.01 K of SUHII and 80 of SUHIF, possibly coupling with heat waves, increases the risk of heat-related diseases. (3) K-means clustering is employed to conduct pattern classification of the selected cities. SUHIF is found possibly to be consistent to its SUHII in the same city, while they provide quantitative and temporal characters respectively. (4) Controls for SUHIF and SUHII are found in significant variations among temporal scales and different patterns. This paper first conducts a comparison between SUHII and SUHIF, and provides pattern classification for further research and practice on mitigation measurements. [ABSTRACT FROM AUTHOR]

Satellite remote sensing allows many hydrological variables to be retrieved at regional and global scales, and these retrievals can contribute to our knowledge of these quantities and their spatial and temporal variation. However, the availability and precision of measurements from remote sensing are still limited. Other important sources of information are represented by reanalysis products and simulations from land surface models (LSMs), which are continuously improving their ability to reproduce the terrestrial hydrological and energy cycles. Such global datasets are very informative and they can be used to integrate the data provided by remote sensing. Among these datasets, the NCEP Climate Forecast System Reanalysis (CFSR) provides many hydrological variables covering the period from 1979 to 2011 with hourly temporal resolution and ~ 0.3-degree spatial resolution. This paper addresses the frequent challenge arising from remote sensing data: how can we create a long-term, global dataset starting with remote sensing data that are sparse in space and time, and provided by sensors flown on different satellites? This was the challenge for creating a long-term Land Surface Temperature (LST) dataset at an hourly resolution based on the High-Resolution Infrared Radiation Sounder (HIRS). To this end, a methodology is presented that merges the continuous CFSR LST estimates with the HIRS retrievals that come from 11 different NOAA satellites. The goals of the analysis are to have a long-term LST dataset consistent with the HIRS retrievals, and to assess the uncertainty associated with the reconstructed data through a Bayesian uncertainty post-processing methodology. The result of this approach is a temporally and spatially continuous dataset, with no step changes associated to the different NOAA satellites, and a dataset described in terms of a probability distribution function whose expected value is unbiased with respect to the original HIRS LST retrievals. [ABSTRACT FROM AUTHOR]

Human activities amplified by climate change pose a significant threat to the sustainability of water resources. Coupled climate-hydrologic simulations commonly predict these threats by combining shallow 1-D land surface models (LSMs) with traditional 2-D and 3-D hydrology models. However, these coupled models limit the moisture and energy-feedback dynamics to the shallow near-surface. This paper presents a novel analysis by applying an integrated variably-saturated subsurface/surface hydrology and heat transport model, HydroGeoSphere (HGS), as a land surface model (LSM). Furthermore, this article demonstrates the coupling of HGS to a simple 0-D atmospheric boundary layer (ABL) model. We then applied our coupled HGS-ABL model to three separate test cases and reproduced the strong correlation between the atmospheric energy balance to the depth of the groundwater table. From our simulations, we found that conventional LSMs may overestimate surface temperatures for extended drought periods because they underestimate the heat storage in the groundwater zone. Our final test case of the atmospheric response to drought conditions illustrated that deeper roots buffered the atmosphere better than shallow roots by maintaining higher latent heat fluxes, lower sensible heat fluxes, and lower surface and atmospheric temperatures. [ABSTRACT FROM AUTHOR]

Forest fires are one of the most important causes of environmental alteration in Mediterranean countries. Discrimination of different degrees of burn severity is critical for improving management of fire-affected areas. This paper aims to evaluate the usefulness of land surface temperature (LST) as potential indicator of burn severity. We used a large convention-dominated wildfire, which occurred on 19–21 September, 2012 in Northwestern Spain. From this area, a 1-year series of six LST images were generated from Landsat 7 Enhanced Thematic Mapper (ETM+) data using a single channel algorithm. Further, the Composite Burn Index (CBI) was measured in 111 field plots to identify the burn severity level (low, moderate, and high). Evaluation of the potential relationship between post-fire LST and ground measured CBI was performed by both correlation analysis and regression models. Correlation coefficients were higher in the immediate post-fire LST images, but decreased during the fall of 2012 and increased again with a second maximum value in summer, 2013. A linear regression model between post-fire LST and CBI allowed us to represent spatially predicted CBI ( R -squared adj > 85%). After performing an analysis of variance (ANOVA) between post-fire LST and CBI, a Fisher's least significant difference test determined that two burn severity levels (low-moderate and high) could be statistically distinguished. The identification of such burn severity levels is sufficient and useful to forest managers. We conclude that summer post-fire LST from moderate resolution satellite data may be considered as a valuable indicator of burn severity for large fires in Mediterranean forest ecosytems. [ABSTRACT FROM AUTHOR]

In this paper, land surface observation data at semi-arid climate and environmental observation station (SACOL station) of Lanzhou University during 2006 -2012 and the data of Yuzhong meteorological station were used to analyze the responses of land surface radiation budget components to climate fluctuation, study the interannual variability of surface albedo, and discuss the feedback of various land surface process parameters on the interannual fluctuations of temperature and precipitation in Loess Plateau. According to the type of precipitation in Loess Plateau, the year was divided into winter and summer in order to get more significant interannual variability and correlation. The results showed that the trends of temperature and precipitation during 2006 -2012 were consistent with the warming and drying total trend in recent years in Loess Plateau. Shallow surface soil moisture and temperature showed a good response to temperature and precipitation, and the annual variation of summer half year had greater impact on the trend in the whole year. Incident solar radiation increase was major reason for climate warming in the Loess Plateau region. The combined effect of climatic factors was the reason for the change of surface albedo. Through the distinguish inquiry by winter and summer data, it was obtained that most correlations between summer radiation components and climatic factors have been improved, and partial correlations between winter radiation components and climatic factors have been increased. [ABSTRACT FROM AUTHOR]

Terrestrial vegetation is one of the most important components of the Earth's land surface. Variations in terrestrial vegetation directly impact the Earth system's balance of material and energy. This paper describes detected variations in vegetation activity at a national scale for China based on nearly 30 years of remote sensing data derived from NOAA/AVHRR (1982-2006) and MODIS (2001-2009). Vegetation activity is analyzed for four regions covering agriculture, forests, grasslands, and China's Northwest region with sparse vegetation cover (including regions without vegetation). Relationships between variations in vegetation activity and climate change as well as agricultural production are also explored. The results show that vegetation activity has generally increased across large areas, especially during the most recent decade. The variations in vegetation activity have been driven primarily by human factors, especially in the southern forest region and the Northwest region with sparse vegetation cover. The results further show that the variations in vegetation activity have influenced agricultural production, but with a certain time lag. [ABSTRACT FROM AUTHOR]

• LUE-VPM produced better quality NPP than MODIS products. • PAR produced from high-resolution DEM can be used in regional NPP modelling. • The MODIS NPP is statistical different with the in-situ measurements in Zeku implementation. Accurate estimation of grassland Net Primary Productivity (NPP) is one of the most important steps in monitoring and further analysing the status of the grassland ecosystem. However, the quality of regional or even global grassland NPP estimations are commonly limited by the two most important input datasets: Photosynthetic Active Radiation (PAR) and climatic data, which make the NPP estimate unrepresentative or even inaccurate. This paper uses a Light Use Efficiency with Vegetation Photosynthesis Model (LUE-VPM) to estimate the high-quality grassland NPP in Zeku. By using the PAR data derived from high resolution DEM products and the improved land surface temperature data, the NPP produced by LUE-VPM shows that it is statistically the same as in situ converted NPP with a p-value of t -test equalling 0.90 (the RMSE between the two is 97.77 gC/m2), while MODIS NPP in Zeku is statically different from the all the in-situ sampling results with a p-value of t-text smaller than 0.03. The results of this paper provided a better estimate than MODIS NPP, and LUE-VPM can be used for the high-quality regional grassland NPP estimation when the quality of PAR data and climatic data improved. [ABSTRACT FROM AUTHOR]

In the acceleration of industrialization and urbanization, brownfield is an important resource for land regeneration and restoration targeting to recover its ecosystem function. Landfills, as a type of brownfield distributed in the highly populated areas, occupy massive land resources. The ecological restoration of landfills becomes an engineering approach to achieve multiple effects of society, environment, and economy. This paper aims to monitor and assess the restoration microclimatic effects of landfills restoration in China with remote sensing data and techniques. We selected representative landfill restoration projects, including Nanhaizi Park in Beijing, Nanhu Park in Tangshan, and Tianziling Park in Hangzhou, as the study cases. Using the Landsat imagery, the scenes of the pre-restoration, mid-restoration, and post-restoration were retrieved, and the surface energy balance equation was applied to calculate normalized difference vegetation index (NDVI), land surface temperature (T s), and evapotranspiration (ET), representing vegetation conditions and hydro-heat microclimate conditions. The study areas are divided into four subzones and extracted the calculated values for statistics and comparison. We defined an indicator of restoration intensity (RI) using the different subzones to the outer contour distance radius and restoration phases to evaluate the spatial-temporal restoration degrees. The spatial pattern dynamics was displayed in the urbanization context in the cases of Beijing and Tangshan. We examined and quantified the relations between RI and the key indicators, NDVI, T s , and ET. The overall results revealed that with higher restoration intensity, NDVI and ET became higher, and T s became lower. It suggested that the ecological restoration on the landfills brought better vegetation, cooling, and humidification effects for the microclimatic environment. Our research has the implications for monitoring and assessing the climatic effects of ecological restoration in the brownfield regeneration in a macroscopic and ecosystem perspective. Unlabelled Image • With satellite dataset, we developed a way to monitor vegetation and microclimatic changes in the restored landfills. • The indicator to quantify the ecological restoration intensity was invented with spatial distance and restoration phase. • Overall, with higher restoration intensity, vegetation and evapotranspiration improved, and surface temperature declined. [ABSTRACT FROM AUTHOR]

This is an update of Sobrino et al.'s paper, published in January 2020, which extends the calculation of the Earth's surface temperature to the period 2003–2019 and uses the new version 2019.0 for the sea surface temperature product MODIS, which is available from 15 January 2020 and replaces version 2014.0. The land surface temperature was estimated from the MCD11C1 product for the same period. The results corroborate the temperature anomalies retrieved from climate models and improve the comparison with global annual air temperatures estimated by the NOAA's National Climatic Data Center (NOAA-NCDC), with a correlation coefficient of 0.96. In addition, a trend of 0.021 ± 0.001 °C/year increase was found for the Earth's surface temperature in this 17-year period. [ABSTRACT FROM AUTHOR]

As an essential climate variable (ECV), land surface albedo plays an important role in the Earth surface radiation budget and regional or global climate change. The Tibetan Plateau (TP) is a sensitive environment to climate change, and understanding its albedo seasonal and inter-annual variations is thus important to help capture the climate change rules. In this paper, we analyzed the large-scale spatial patterns, temporal trends, and seasonal variability of land surface albedo overall the TP, based on the moderate resolution imaging spectroradiometer (MODIS) MCD43 albedo products from 2001 to 2019. Specifically, we assessed the correlations between the albedo anomaly and the anomalies of normalized difference vegetation index (NDVI), the fraction of snow cover (snow cover), and land surface temperature (LST). The results show that there are larger albedo variations distributed in the mountainous terrain of the TP. Approximately 10.06% of the land surface is identified to have been influenced by the significant albedo variation from the year 2001 to 2019. The yearly averaged albedo was decreased significantly at a rate of 0.0007 (Sen's slope) over the TP. Additionally, the yearly average snow cover was decreased at a rate of 0.0756. However, the yearly average NDVI and LST were increased with slopes of 0.0004 and 0.0253 over the TP, respectively. The relative radiative forcing (RRF) caused by the land cover change (LCC) is larger than that caused by gradual albedo variation in steady land cover types. Overall, the RRF due to gradual albedo variation varied from 0.0005 to 0.0170 W/m2, and the RRF due to LCC variation varied from 0.0037 to 0.0243 W/m2 during the years 2001 to 2019. The positive RRF caused by gradual albedo variation or the LCC can strengthen the warming effects in the TP. The impact of the gradual albedo variations occurring in the steady land cover types was very low between 2001 and 2019 because the time series was short, and it therefore cannot be neglected when examining radiative forcing for a long time series regarding climate change. [ABSTRACT FROM AUTHOR]

Methane, CH 4 , is a powerful greenhouse gas released to the atmosphere by both anthropogenic and natural sources. Urban areas play an important role in greenhouse gas emissions, yet few efforts have been made to investigate CH 4 concentration and its spatial variability in cities. In this paper, a case study to monitor the spatial pattern and determinants of near-surface urban CH 4 concentration was conducted in Shanghai, the commercial and financial center of mainland China. A total of 173 sampling points were collected to examine the atmospheric CH 4 concentration across different land use types and urban-rural gradients at a height of 2 m above the ground in April and May 2014. The contributions of potential influencing factors, including four indicators of human activities (e.g., distance to natural gas pipelines [Dist_gas], distance to sewage plants [Dist_sew], population density [Den_pop], density of road network [Den_road]) and two indicators of natural conditions (i.e., planetary boundary layer height [BLH] and land surface temperature [LST]), in determining spatial CH 4 concentrations were investigated using Lindeman-Merenda-Gold (LMG) metric. The results indicated that the average CH 4 concentration in the morning for the region was 1,897 ± 92 parts per billion by volume (ppbv), with significant spatial heterogeneity. Atmospheric CH 4 had negative correlation with the distance to urban center except the influence of obvious emission sources, such as petrochemical plants etc. The six selected influencing factors could explain 76.3% of the CH 4 spatial variability. Population density was the dominant factor for about 27.2%, while the distance to the natural gas pipelines, planetary boundary layer height and distance to the sewage plants were about 14.4%, 14.2% and 12.6%, respectively. The finding in this study might help to build a better understanding of the mechanics of CH 4 variations and evaluation of the potentially influencing factors. • Atmospheric CH 4 concentration over Shanghai was investigated. • The averaged CH 4 was 1,897 ± 92 ppbv with significant spatial heterogeneity. • CH 4 had negative correlation with the distance to urban center. • The selected predictors could explain 76.3% of the CH 4 spatial variability. [ABSTRACT FROM AUTHOR]

In the Tibetan Plateau (TP), the changes of lake ice phenology not only reflect regional climate change, but also impose substantial ecohydrological impacts on the local environment. Due to the limitation of ground observation, remote sensing has been used as an alternative tool to investigate recent changes of lake ice phenology. However, uncertainties exist in the remotely sensed lake ice phenology owing to both the data and methods used. In this paper, three different remotely sensed datasets are used to investigate the lake ice phenology variation in the past decade across the Tibetan Plateau, with the consideration of the underlying uncertainties. The remotely sensed data used include reflectance data, snow product, and land surface temperature (LST) data of moderate resolution imaging spectroradiometer (MODIS). The uncertainties of the three methods based on the corresponding data are assessed using the triple collocation approach. Comparatively, it is found that the method based on reflectance data outperforms the other two methods. The three methods are more consistent in determining the thawing dates rather than the freezing dates of lake ice. It is consistently shown by the three methods that the ice-covering duration in the northern part of the TP lasts longer than that in the south. Though there is no general trend of lake ice phenology across the TP for the period of 2000–2015, the warmer climate and stronger wind have led to the earlier break-up of lake ice. [ABSTRACT FROM AUTHOR]

The objective of the paper was to characterize the occurrence of cold days and cold waves in Poznań in the years 1966/67–2015/16, as well as to characterize thermal conditions in the city during selected cold waves in the years 2008/09–2015/16. The study was based on daily data on maximum and minimum air temperature for station Poznań-Ławica from the years 1966/67–2015/16 and daily air temperature values from eight measurement points located in the territory of the city in different types of land use from the years 2008/08–2015/16. In addition, to characterize thermal conditions during selected days forming cold waves, satellite images were used, on the basis of which the land surface temperature (LST) was calculated. A cold day was defined as a day with daily maximum temperature (Tmax) below the value of 5th annual percentile of Tmax, and a cold wave was defined as at least five consecutive cold days. The study showed an increase in Tmax in winter, which translated to a decrease in the number of cold days over the last 50 years, although the changes were not statistically significant. Thermal conditions in the city showed high variability in the winter season and during the analyzed cold waves. [ABSTRACT FROM AUTHOR]