Climate Change Studies Research Articles

Evaluation of ERA5-Simulated Temperature and Its Extremes for Australia

Atmospheric reanalysis products offer high-resolution and long-term gridded datasets that can often be used as an alternative or a supplement to observational data. Although more accessible than typical observational data and deemed fit for climate change studies, reanalysis data can show biases resulting from data assimilation approaches. Thus, a thorough evaluation of the reanalysis product over the region and metric of study is critical. Here, we evaluate the performance of the latest generation of ECMWF reanalysis, ERA5, in simulating mean and extreme temperatures over Australia for 1979–2020 versus high-quality gridded observations. We find ERA5 generally simulates maximum and minimum temperatures reasonably well (mean bias ~1.5 °C), even though it underestimates/overestimates the daily maximum/minimum temperatures, leading to a cold bias for Tmax and a warm bias for Tmin. ERA5 also underestimates the decadal warming trend in both Tmax and Tmin compared to the observations. Furthermore, ERA5 struggles to simulate the temporal variability of Tmin, leading to a markedly worse skill in Tmin than Tmax. In terms of extreme indices, ERA5 is skilled at capturing the spatial and temporal patterns and trends of extremes, albeit with the presence of biases in each index. This can partially be attributed to the warm bias in the minimum temperature. Overall, ERA5 captures the mean and extreme temperature indices over the Australian continent reasonably well, warranting its potential to supplement observations in aiding climate change-related studies, downscaling for boundary conditions, and climate model evaluation.

How to measure mast seeding?

The periodic production of large seed crops, or masting, is a widespread phenomenon in perennial plants. This behavior can enhance the reproductive efficiency of plants, leading to increased fitness, and produce ripple effects on food webs. While variability from year to year is a defining characteristic of masting, the methods used to quantify this variability are highly debated. The commonly used coefficient of variation lacks the ability to account for the serial dependence in mast data and can be influenced by zeros, making it a less suitable choice for various applications based on individual-level observations, such as phenotypic selection, heritability, and climate change studies, which rely on individual-plant-level datasets that often contain numerous zeros. To address these limitations, we present three case studies and introduce volatility and periodicity, which account for the variance in the frequency domain by emphasizing the significance of long intervals in masting. By utilizing examples of Sorbus aucuparia, Pinus pinea, Quercus robur, Quercus pubescens, and Fagus sylvatica, we demonstrate how volatility captures the effects of variance at both high and low frequencies, even in the presence of zeros, leading to improved ecological interpretations of the results. The growing availability of long-term, individual-plant datasets promises significant advancements in the field, but requires appropriate tools for analysis, which the new metrics provide.

Application of hydrological model to simulate streamflow contribution on water balance in Himalaya river basin, Nepal

Hydrological models are widely used and often regarded as reliable tools for accurately estimating various components of the water balance. In a remote Himalayan catchment, such as Tamakoshi basin, where limited hydrometric dataset is available, such models often provide essential insights that are crucial to water researchers and planners. In this regard, we employed the semi-distributed HBV-light (version 4.0.0.25) hydrological model for glacierized Tamakoshi river basin and attempted to quantify various water balance components. For our model tests, using the daily streamflow records, we selected two distinct periods, i.e., 2004–2008 as a calibration period whilst 2011–2012 for model validation. Based on our findings, the model was able to reasonably predict the streamflow (validation efficiency: Nash-Sutcliffe Efficiency of 0.82 and percent bias −21%). At our site, HBV-light model predicted that the change in streamflow was mostly governed by monsoonal rain (62%) followed by baseflow (20%), glacier melt (13%) and snowmelt (5%). As expected, the streamflow peaked during the month of August where monsoon-induced rain and melting of glaciers significantly contributed to river flow. As a result, monsoon period showcased largest fluctuation in water storage while negligible change was observed during post-monsoon season. Nonetheless, our findings revealed that the baseflow contribution to streamflow was maximum during the month of October and lowest during February. Our findings indicated that the water balance of the Tamakoshi basin is largely influenced by monsoonal rain during June–September window as well as baseflow and glacier melt during the dry season. Runoff components contribution to streamflow was increasing but water storage changes was decreasing in recent decade (2011–2020). We believe our findings are crucial for future initiatives involving water resources, water-induced disaster management, and studies of climate change may benefit from the findings of this study, especially in a region with limited hydrometric data availability.

Ensure forest-data integrity for climate change studies

Ensure forest-data integrity for climate change studies

Micro‐ to macro‐scaling analysis of PM2.5 in sensitive environment of Himalaya, India

Fine particulate matter (PM) in the atmosphere has become a significant air contaminant with substantial health consequences. Although airborne remote sensing and ground sensor monitoring can offer air quality datasets containing PM2.5, there are limitations to effectively analysing large long‐term datasets. The research aims to evaluate air quality over the Himalayan region using the multifractal approach for the PM2.5 data time series. Fractal dimension (FD), Hurtz exponent (H), and predictability (PI) are estimated using the rescaled range. PM2.5 is found to have high concentration and frequency throughout the day. The same is found in the night hours during peak tourism months. The hourly PM2.5 time series datasets have shown multifractality. The primary reason for this is emissions produced by vehicles and anthropocentric activities in the region. The H is used to assess the dynamic features of the PM2.5 time series in terms of persistence and self‐correlation. In the context of climate change studies, it is crucial to monitor the spatial distribution and dynamic behaviour of PM2.5 in the Himalayan foothills. This study aims to provide prediction analyses and air quality index (AQI) estimates and demonstrate how PM2.5 concentrations alter the sensitive environment throughout the micro to macro scale. This will help us to build a long‐term strategy for reducing the harmful effect of increasing pollution levels on the ecosystem and human health.

Downscaling wind speed based on coupled environmental factors and machine learning

AbstractWind speed changes impact society and have important implications for climate change studies. Thus, high‐resolution and high‐quality wind speed datasets are necessary for environmental monitoring and ecosystem research. However, there is no complete set of high spatial and temporal resolution wind speed datasets for China. Additionally, it is extremely challenging to produce wind speed data at high spatial and temporal resolution for large‐scale regions with diverse climate types and complex topographies, such as China. In this study, we used multisource remote sensing images, obtained data on various environmental factors through the Google Earth Engine and Evapotranspiration (ET) Watch Cloud platforms, and combined machine learning algorithms to downscale the ERA5 reanalysis wind speed data, and finally obtained the daily wind speed datasets with 1 km spatial resolution for China in 2015. To verify the accuracy of the model and data products, we selected several metrics to evaluate in conjunction with the actual site observed data. The results show that the multifactor combination model of artificial neural network combining land surface temperature, sunshine durations and roughness factors outperforms a single‐factor combination model, and the results were in good agreement with the original data (R2 of 0.95 and RMSE of 0.40 m·s−1). The final wind speed data products were also in good agreement with the observed meteorological data (R2 range of 0.86–0.95 and RMSE range of 0.33–0.44 m·s−1); moreover, the accuracy and precision were greatly improved over the original data. This study provided a dataset that has potential applications in future climate change and ecosystem studies.

Spatial and Temporal Variations of Arctic Sea Ice during 1992-2021

The variation of polar sea ice is an indicator of polar environmental change, which plays an important role in the study of regional and global climate change. The arctic is a region in transition to a warmer climate and one of the most visible signs of that change is in the declining sea ice cover. Sea ice in the arctic is one of the most rapidly changing components of the global climate system. The extent of area covered by arctic sea ice is an important indicator of changes in global climate because warmer air and water temperatures are reducing the amount of sea ice present. Sea ice extent plays an important role in reflecting sunlight back into space, regulating ocean and air temperatures, circulating ocean water, maintaining animal habitats. Similarly, sea ice concentration helps to determine the number of other important climate variables. In this paper, the sea ice data from different institutes were used to analyze the spatial and temporal variation of arctic sea ice concentration (SIC) and sea ice extent (SIE) over the 30-year period 1992-2021. During this period, the results shows that these two variables were increased in the arctic during 2021 due to the decrease of temperatures particularly near Chukchi, Kara, Beaufort, East Siberian and Greenland Seas.

Applicability Assessment of Passive Microwave LST Downscaling over Semi–Homogeneous Desert Underlying Surface Based on Machine Learning

The spatial and temporal resolution of remote sensing products in land surface temperature (LST) studies can be improved using the downscaling method. This is a crucial area of research as it provides basic data for the study of climate change. However, there have been few studies evaluating the applicability of downscaling methods using underlying surfaces of varying complexities. In this study, we focused on the semi–homogeneous underlying surface of Gurbantunggut Desert and evaluated the applicability of five classical, passive microwave, downscaling methods based on the machine learning of Catboost, using 365 days of AMSR–2 and MODIS data in 2019, which can be scanned once during the day and night. Our results showed four main points: (1) The correlation coefficients between feature vectors and the LST of the semi–homogeneous underlying surface were clearly different from those of the surrounding oases. The correlation coefficient of the semi–homogeneous underlying surface was high, and that of the surrounding oases was low. (2) At the same frequency, the correlation coefficient between vertically polarized BT and LST was greater than that between horizontally polarized BT and LST. Considering the semi–heterogeneous underlying surface, 23.8 GHz and 36.5 GHz may be more suitable for passive microwave LST retrieval than 89 GHz according to physical mechanisms. (3) The fine–scale LST downscaling accuracy achieved with all BT channels of AMSR–2 was higher than that achieved with the other four classical models. The day and night RMSE values verified with MYD11A1 data were 2.82 K and 1.38 K, respectively. (4) The correlation coefficients between downscaled LST and the soil temperature of the top layer of the site were the highest, with daytime–nighttime R2 values of 0.978 and 0.970, and RMSE values of 3.42 and 4.99 K, respectively. The all–channel–based LST downscaling method is very effective and can provide a theoretical foundation for the acquisition of all–weather, multi–layer soil temperature.

Effects of environment factors on the carbon fluxes of semi-fixed sandy land recovering from degradation

Shrub-dominated ecosystems in the semiarid Horqin Sandy Land are important terrestrial ecosystems, and substantially affect global ecological health and security. However, there have been few studies of climate change’s effects on the carbon fluxes (NEE, net ecosystem exchange; Reco, ecosystem respiration; GPP, gross primary productivity) when these ecosystems are recovering from degradation. We used the eddy covariance technique to determine carbon fluxes and climatic conditions in this ecosystem from 2017 to 2021. The semi-fixed sandy land functioned as a carbon sink in wet years (NEE equaled −14.14 and −126.14 g C m–2 yr–1 in 2019 and 2021, respectively), but was a carbon source in dry years (NEE equaled 48.50 and 51.17 g C m–2 yr–1 in 2017 and 2020, respectively) and a normal year (NEE equaled 74.66 g C m–2 yr–1 in 2018). As expected in these usually water-limited ecosystems, water availability (precipitation and soil water content) were the dominant drivers of NEE, Reco, and GPP, but temperature and photosynthetic photon flux density (PPFD) also played important roles in regulating NEE, Reco and GPP in this recovering semi-fixed sandy ecosystem. With future precipitation and temperature increases, and continuing vegetation restoration, carbon sequestration by this ecosystem is expected to increase. Long-term observations will be necessary to reveal the true source and sink intensities and their response to environmental factors.

Weathering Intensity Response to Climate Change on Decadal Scales: A Record of Rb/Sr Ratios from Chaonaqiu Lake Sediments, Western Chinese Loess Plateau

The Rb/Sr ratio of lake sediments has been widely adopted as an indicator of weathering intensity in studies of past climate change, but the geochemical significance of this ratio varies with timescale. Here, we present Rb/Sr data for the past 300 years for sediments collected from Chaonaqiu Lake in the Liupan Mountains of the western Chinese Loess Plateau as a decadal-scale record of weathering intensity. To validate the application of this weathering proxy, we correlated the record with those of other major elements, rock-forming minerals, and paleoclimatic proxies. We found that Rb/Sr ratios are influenced mainly by Sr activity within the lake catchment (where Sr is likely sourced from albite). In addition, higher (lower) Rb/Sr ratios of bulk sediments from Chaonaqiu Lake are correlated with lower (higher) fractions of terrigenous detritus (SiO2, Ti, K2O, Al2O3, and Na2O). These indicate that the Rb/Sr ratios of bulk sediments in Chaonaqiu Lake are closely linked to terrigenous detritus input on decadal scales and also correlate well with TOC (a precipitation indicator) and other high-resolution paleoclimate records (e.g., tree rings and drought/flood index) in neighboring regions, with higher (lower) Rb/Sr ratios corresponding to more (less) precipitation. Lake bulk sediment Rb/Sr ratios are dominated by the input of terrigenous detritus over decadal timescales. Our data show that physical and chemical weathering in the Chaonaqiu Lake watershed have opposing influences on Rb/Sr ratios of bulk sediment, competing to dominate these ratios of lake sediments over different timescales, with ratios reflecting the relative importance of the two types of weathering.

A Language Spoken with Words: Decolonization, Knowledge Production and Environmental Injustice in the Work of Abdulrazak Gurnah

The paper analyses the relationship between decolonization, climate change, and environmental injustice as represented in the writings of Abdulrazak Gurnah. Gurnah’s work is considered an example of decolonial literature. Decolonial literature has focused on issues beyond the nature of the colonial subject, highlighting the relationship between the capitalist world economy and the formation of modern decolonial subjectivities, namely the exposure of those subjectivities to environmental injustice. The paper intends to answer the following research question: how does Abdulrazak Gurnah address the articulation between decolonization, climate change, and environmental injustice? The paper argues that Gurnah addresses such an articulation by discussing knowledge production about the colonial subject and the postcolonial self and instituting an association between environmental/climate precarity and biopolitical precarity. Building from two of Gurnah’s novels—By the Sea and Afterlives – the paper debates how Gurnah’s characters are afflicted by the racialization of social and economic relations and biopolitical and climate precarity. Questioning knowledge production about the colonial subject and the postcolonial self is significant because it underscores the importance of transforming how knowledge about climate change is produced. Instituting an association between environmental/climate precarity and biopolitical precarity permits debating how colonial and capitalist power structures are responsible for disseminating environmental injustice, foregrounding the epistemic importance of indigenous climate change studies. The work of Gurnah is critically analyzed, bearing in mind the need to discuss the relevance of addressing climate change and environmental injustice from the perspective of global south literature.

Comparison of Various Annual Land Cover Datasets in the Yellow River Basin

Accurate land cover (LC) datasets are the basis for global environmental and climate change studies. Recently, numerous open-source annual LC datasets have been created due to advances in remote sensing technology. However, the agreements and sources of error that affect the accuracy of current annual LC datasets are not well understood, which limits the widespread use of these datasets. We compared four annual LC datasets, namely the CLCD, MCD12Q1, CCI-LC, and GLASS-LC, in the Yellow River Basin (YRB) to identify their spatial and temporal agreement for nine LC classes and to analyze their sources of error. The Mann–Kendall test, Sen’s slope analysis, Taylor diagram, and error decomposition analysis were used in this study. Our results showed that the main LC classes in the four datasets were grassland and cropland (total area percentage > 80%), but their trends in area of change were different. For the main LC classes, the temporal agreement was the highest between the CCI-LC and CLCD (0.85), followed by the MCD12Q1 (0.21), while the lowest was between the GLASS-LC and CLCD (−0.11). The spatial distribution of area for the main LC classes was largely similar between the four datasets, but the spatial agreement in their trends in area of change varied considerably. The spatial variation in the trends in area of change for the cropland, forest, grassland, barren, and impervious LC classes were mainly located in the upstream area region (UA) and the midstream area region (MA) of the YRB, where the percentage of systematic error was high (>68.55%). This indicated that the spatial variation between the four datasets was mainly caused by systematic errors. Between the four datasets, the total error increased along with landscape heterogeneity. These results not only improve our understanding of the spatial and temporal agreement and sources of error between the various current annual LC datasets, but also provide support for land policy making in the YRB.

Exceptional stratospheric contribution to human fingerprints on atmospheric temperature

In 1967, scientists used a simple climate model to predict that human-caused increases in atmospheric CO2 should warm Earth's troposphere and cool the stratosphere. This important signature of anthropogenic climate change has been documented in weather balloon and satellite temperature measurements extending from near-surface to the lower stratosphere. Stratospheric cooling has also been confirmed in the mid to upper stratosphere, a layer extending from roughly 25 to 50 km above the Earth's surface (S25 - 50). To date, however, S25 - 50 temperatures have not been used in pattern-based attribution studies of anthropogenic climate change. Here, we perform such a "fingerprint" study with satellite-derived patterns of temperature change that extend from the lower troposphere to the upper stratosphere. Including S25 - 50 information increases signal-to-noise ratios by a factor of five, markedly enhancing fingerprint detectability. Key features of this global-scale human fingerprint include stratospheric cooling and tropospheric warming at all latitudes, with stratospheric cooling amplifying with height. In contrast, the dominant modes of internal variability in S25 - 50 have smaller-scale temperature changes and lack uniform sign. These pronounced spatial differences between S25 - 50 signal and noise patterns are accompanied by large cooling of S25 - 50 (1 to 2[Formula: see text]C over 1986 to 2022) and low S25 - 50 noise levels. Our results explain why extending "vertical fingerprinting" to the mid to upper stratosphere yields incontrovertible evidence of human effects on the thermal structure of Earth's atmosphere.

The extremely hot and dry 2018 summer in central and northern Europe from a multi-faceted weather and climate perspective

Abstract. The summer of 2018 was an extraordinary season in climatological terms for northern and central Europe, bringing simultaneous, widespread, and concurrent heat and drought extremes in large parts of the continent with extensive impacts on agriculture, forests, water supply, and the socio-economic sector. Here, we present a comprehensive, multi-faceted analysis of the 2018 extreme summer in terms of heat and drought in central and northern Europe, with a particular focus on Germany. The heatwave first affected Scandinavia in mid-July and shifted towards central Europe in late July, while Iberia was primarily affected in early August. The atmospheric circulation was characterized by strongly positive blocking anomalies over Europe, in combination with a positive summer North Atlantic Oscillation and a double jet stream configuration before the initiation of the heatwave. In terms of possible precursors common to previous European heatwaves, the Eurasian double-jet structure and a tripolar sea surface temperature anomaly over the North Atlantic were already identified in spring. While in the early stages over Scandinavia the air masses at mid and upper levels were often of a remote, maritime origin, at later stages over Iberia the air masses primarily had a local-to-regional origin. The drought affected Germany the most, starting with warmer than average conditions in spring, associated with enhanced latent heat release that initiated a severe depletion of soil moisture. During summer, a continued precipitation deficit exacerbated the problem, leading to hydrological and agricultural drought. A probabilistic attribution assessment of the heatwave in Germany showed that such events of prolonged heat have become more likely due to anthropogenic global warming. Regarding future projections, an extreme summer such as that of 2018 is expected to occur every 2 out of 3 years in Europe in a +1.5 ∘C warmer world and virtually every single year in a +2 ∘C warmer world. With such large-scale and impactful extreme events becoming more frequent and intense under anthropogenic climate change, comprehensive and multi-faceted studies like the one presented here quantify the multitude of their effects and provide valuable information as a basis for adaptation and mitigation strategies.

Origin and significance of ultra-slow calcite dissolution rates in deep sea sediments

Prediction of mineral-fluid reaction rates in geologic materials is subject to large uncertainties even though the rates are critical for many applications, from studies of past climate change to the engineering of carbon removal and storage. Deep sea carbonate sediments, which have been extensively cored and characterized at dozens of sites by ocean drilling programs, provide unique opportunities to examine how minerals and fluids react over multi-million-year timescales in natural conditions at low temperature. It has already been established that the calcite-fluid reaction in these systems is extremely slow, but the explanation for the ultra-slow rates, and the fact that non-zero rates persist for tens of millions of years, remains elusive. We extend the analysis of pore fluid Sr and Ca concentration data in the literature from ocean drilling archives to 21 drill sites in sediment sections where the fraction of carbonate is larger than 80% to systematically estimate the rates at which calcite dissolves and precipitates as a function of depth and sediment age. Pore fluid Sr/Ca used in this study, and Sr and Ca isotopes in other published analyses, provide estimates of the gross calcite dissolution rates, which are mostly balanced by secondary calcite precipitation. Pore fluid Ca concentration data provide key, previously underappreciated, information on the net calcite dissolution rates. Using these data we evaluate simple models for calcite reaction kinetics and pore fluid sediment evolution and conclude that (1) reaction rates are extremely slow because the reactive site density of the bulk sediment decreases continuously and systematically approximately as age−1, and (2) reaction persists to hundreds of meters burial depth and tens of millions of years because pore fluids never become closed due to diffusive communication with the oceans, and hence remain slightly undersaturated with respect to calcite. The systematic decrease in bulk reactive site density begins immediately upon deposition, greatly slowing the rate of dissolution and hence affecting models of calcite dissolution on the seafloor. The failure to reach equilibrium after millions of years and hundreds of meters of burial, is a consequence of extremely slow net reaction rates that cannot drive the pore fluids to equilibrium against the diffusive loss of Ca and DIC to the ocean. The existence of two or more calcite phases of different solubility is a likely source of a long-lived, but small, driving force for reaction, but its effect decreases with continued recrystallization and pore fluid evolution.

Farmers coping strategies to climate change in sustainable Arabica coffee production

Geographical Indications of Gayo Arabica coffee is currently faced with various challenges, including climate change, causing stagnation and, eventually, a decline in productivity. This research aims to determine the strategies adopted by farmers in dealing with climate change, to increase and maintain Gayo coffee productivity. The research was conducted in the Gayo Highlands, particularly in Aceh Tengah, Bener Meriah, and Gayo Lues districts, in three stages, including; (1) a study of the Gayo coffee GI area, (2) climate change study, and (3) a study of farmers’ strategies for dealing with climate change, using field observation surveys and interviews with farmers. The results reveal that the GI of Gayo arabica coffee has been mapped since its designation in 2010. Additionally, both rainfall and temperature have changed since the 1980s, and climate change (particularly temperature) has resulted in habitat shifting for the cultivation of arabica coffee clones, coffee berry borer infestations, and a decrease in coffee productivity. Farmers in the Gayo Arabica coffee GI area address climate change by implementing a strategy to create a microclimate through adaptation and mitigation. These include efforts to adapt coffee clones to their growing habitat, creating a microclimate by planting coffee and denser shade, covering the soil surface to minimize evaporation, and reducing the concentration of carbon dioxide gas and greenhouse gas emissions in the air by using locally sourced organic matter as fertilizer.

Preface

Following the success of 2014, 2016, 2018, and 2021 International Conference on Science & Technology Applications in Climate Change (STACLIM), the Institute of Climate Change (IPI), Universiti Kebangsaan Malaysia (UKM) is proud to extend our promotion of research and education for the advancement of climate change studies. The 2022 International Conference on Science & Technology Applications in Climate Change (STACLIM 2022) with the theme “Climate change mitigation action through the lens of science and technology” is the fifth in the series of conferences organized by IPI. This year the conference was carried out in virtual form through the Webex platform (29 – 30 November 2022) due to the COVID-19 travel restriction. Through the virtual form, the science community is able to share their research findings in time.The aim of this conference is to bring together researchers in fields of Environmental Science, Health Sustainability, Mathematics, Sustainable Energy, Economic Sustainability, Socio-Cultural Studies, Social Science, Atmospheric Science, and related fields, to present their research findings as well as create new opportunities for future research collaborations. This event is envisaged to witness active participation from various eminent environmental and earth scientists, engineers and students from academia, industry and government sectors for addressing complications associated with climate change and to draw forth novel and ground-breaking initiatives and solutions for climate resilience.The plenary sessions in the main room were opened by two keynote speeches from leading experts including Prof. Dr. Lisa Stein from University of Alberta, Canada on “Microbial Solutions to Mitigating Climate Change”, Prof. Dr. Haruko Kurihara from University of Ryukyus, Japan, on “Ocean acidification impacts on marine ecosystem and its potential mitigation solutions”. As the keynote session was open for public registration, we had participants joining the event. It was then followed by the invited speaker sessions consisting of Prof. Dr. Fredolin Tangang (UKM), Assoc. Prof. Dr. Rawshan Ara Begum (Macquarie University, Australia), Dr. Shantanu Kumar Pani (National Central University, Taiwan) and Mr. Saud Aldrees (University of Oxford, England). The program was then continued with oral presentation of 72 papers in 3 parallel breakout rooms. Each presenter was given up to 15 mins for presentation and Q&A sessions. There were additional 13 non-presenters who joined in during the presentation session. Presenters and participants have attended the conference from their respective countries including Malaysia, Indonesia, Philippines, USA, China, Saudi Arabia, Iraq, Algeria, India, and Ukraine.The conference went well with great support and synergy of the staff and personnel from Institute of Climate Change, UKM. To document and promulgate the research findings and ideas shared, we are very pleased to publish the accepted research papers of STACLIM 2022 in IOP Conference Series: Earth and Environmental Science (EES).The EditorsList of Organizing Committee is available in this Pdf.

A practical method for angular normalization of global MODIS land surface temperature over vegetated surfaces

Land surface temperature (LST) is an essential physical quantity in surface energy balance and a good indicator of exchange of heat and water between the land and atmosphere at regional and global scales. However, the thermal-infrared remotely sensed LST from large swath-width polar-orbiting and geostationary satellite sensors is usually subject to the angular anisotropy, bringing great uncertainties in the application of LST products in many fields such as evapotranspiration estimation, urban thermal environment monitoring, and climate change study. In this study, a practical angular normalization method was developed for the first time for correcting global MODIS off-nadir LST to nadir over vegetated surfaces, using MODIS (Terra + Aqua) products from 2000 to 2020, simultaneous and collocated observations at 213 sites worldwide from the AmeriFlux, FLUXNET, and National Tibetan Plateau/Third Pole Environment Data Center (TPDC), and ERA5-Land reanalysis datasets. Results showed that the angular normalization method could well correct the angular anisotropy of MODIS LST products. Validated against datasets at 213 sites, the angular normalization model showed root mean square errors (RMSE) of 1.57 K, mean bias errors (MBE) of 0 K, and coefficient of determination (R2) of 0.99. In the cross-validation of nadir LST by correcting global MODIS/Terra off-nadir LST products against Sentinel-3A nadir LST products over four typical days in 2020 (February 26, June 1, August 12 and November 16), the angular normalization model presented the RMSE of 2.26 K and the MBE of −1.20 K, in contrast to the RMSE of 3.01 K and the MBE of −2.15 K when MODIS off-nadir LST was compared to the Sentinel-3A nadir LST. The angular anisotropy of global MODIS/Terra LST over vegetated surfaces for the four days of interest ranged between −1.79 to −1.30 K (1% quantile) and 3.64 to 4.37 K (99% quantile), with a median of 0.33 to 0.74 K, and a mean of 0.46 to 0.89 K. The developed angular normalization model is a promising approach to operationally correcting all time-series of global MODIS/Terra and MODIS/Aqua LST products with high accuracies, which is significant to provide a benchmark of angle-consistent MODIS LST product for its further application.

Spatio-temporal variations in trends of vegetation and drought changes in relation to climate variability from 1982 to 2019 based on remote sensing data from East Asia

Studying the significant impacts on vegetation of drought due to global warming is crucial in order to understand its dynamics and interrelationships with temperature, rainfall, and normalized difference vegetation index (NDVI). These factors are linked to excesses drought frequency and severity on the regional scale, and their effect on vegetation remains an important topic for climate change study. East Asia is very sensitive and susceptible to climate change. In this study, we examined the effect of drought on the seasonal variations of vegetation in relation to climate variability and determined which growing seasons are most vulnerable to drought risk; and then explored the spatio-temporal evolution of the trend in drought changes in East Asia from 1982 to 2019. The data were studied using a series of several drought indexes, and the data were then classified using a heat map, box and whisker plot analysis, and principal component analysis. The various drought indexes from January to August improved rapidly, except for vegetation health index (VHI) and temperature condition index (TCI). While these indices were constant in September, they increased again in October, but in December, they showed a descending trend. The seasonal and monthly analysis of the drought indexes and the heat map confirmed that the East Asian region suffered from extreme droughts in 1984, 1993, 2007, and 2012 among the study years. The distribution of the trend in drought changes indicated that more severe drought occurred in the northwestern region than in the southeastern area of East Asia. The drought tendency slope was used to describe the changes in drought events during 1982–2019 in the study region. The correlations among monthly precipitation anomaly percentage (NAP), NDVI, TCI, vegetation condition index (VCI), temperature vegetation drought index (TVDI), and VHI indicated considerably positive correlations, while considerably negative correlations were found among the three pairs of NDVI and VHI, TVDI and VHI, and NDVI and TCI. This ecological and climatic mechanism provides a good basis for the assessment of vegetation and drought-change variations within the East Asian region. This study is a step forward in monitoring the seasonal variation of vegetation and variations in drought dynamics within the East Asian region, which will serve and contribute to the better management of vegetation, disaster risk, and drought in the East Asian region.

Method of Validating Satellite Surface Reflectance Product Using Empirical Line Method

Atmospherically corrected surface reflectance (SR) products are used for reliable monitoring of land surfaces and are the standard products of Landsat sensors. Due to increased demand for SR products, a need exists to verify that the L2C2 (Level-2 Collection-2) SR products are precise and accurate. The Level-2 Collection 2 (L2C2) SR Product has processed satellite imagery data that correct for atmospheric effects such as absorption and scattering, providing a more accurate representation of Earth’s surface. The validation of SR products using ground truth measurement is essential. This study aims to develop and evaluate a validation methodology for satellite SR products. Thus, the Empirical Line Method (ELM) is used here for atmospheric validation of remotely sensed data. Validation is performed using the SR derived from ELM tied to ground truth measurement. Absolute surface reflectance models of Algodones Dunes and the Salton Sea located in North America Sonoran Desert are developed to extend the temporally limited ground truth measurements. This model can give ground truth reflectance in any time frame independent of time constraints. The result of the absolute surface reflectance model of Algodones Dunes indicates that the model predicts the response of Algodones Dunes with an average accuracy of 0.0041 and precision of 0.0063 and gives ground measurements across all multispectral between 350 and 2500 nm. For the Salton Sea, the model predicts the response of the Salton Sea with mean absolute error (MAE) of 0.0035 and gives ground measurements across all multispectral between 350 and 2500 nm. The ELM generates atmospheric coefficients (gain and bias), which are applied to an image to obtain SR. Validation results indicated that for L9-OLI-2, L8-OLI, and L5-TM-SR products, the RMSE range is 0.0019 to 0.0106, 0.0019 to 0.0148 and 0.0026 to 0.0045 reflectance unit, respectively, and accuracy is within 0.0038, 0.0022, and 0.0055 reflectance unit across all spectral bands of L9, L8, and L5, respectively. On average, the validation result showed a strong linear relation between the L2C2 SR products and ELM SR within 0.5 to 1 reflectance units. These results demonstrate the high accuracy and reliability of the L2C2 SR product, providing valuable information for a wide range of remote sensing applications, including land cover and land use mapping, vegetation monitoring, and climate change studies.