Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE

Comparing different profiles to characterize the atmosphere for three MODIS TIR bands

Accurate Land surface temperature (LST) retrievals from sensors aboard orbiting satellites are dependent on the corresponding atmospheric correction, especially in the Thermal InfraRed (TIR) spectral domain (8-14 µm). To remove the atmospheric effects from at-sensor measured radiance in the TIR range it is needed to characterize the atmosphere by means of three specific variables; the upwelling path and the hemispherical downwelling radiances plus the atmospheric transmissivity. Those variables can be derived from the previous knowledge of vertical atmospheric profiles of air temperature and relative humidity at different geo-potential heights and pressures. In this work, the above mentioned atmospheric variables were analyzed for three specific weather station site located in Spain, at three different altitudes. These variables were calculated with atmospheric profiles retrieved from three different sources; The National Centers for Environmental Prediction (NCEP) web-tool atmospheric profiles calculator, the MODIS (MOD07) product and the radiosoundings available on the web of the University of Wyoming (WYO), which are launched by the Agencia Estatal de Meteorologia (AEMET), in the particular case of Spain. Atmospheric profiles from 2010 to 2013 were obtained to carry out the present study. Results from comparison of these three different atmospheric profiles show that the NCEP profiles characterize the atmosphere in a better manner than MOD07. Average results values of the three MODIS spectral bands 29, 31 and 32 show a BIAS of 0.06 Wm-2µm-1sr-1 and RMSE of ±0.2 Wm-2µm-1sr-1 for upwelling radiance, a BIAS of 0.13 Wm-2µm-1sr-1 and RMSE of ±0.3 Wm-2µm-1sr-1 for the donwelling radiance and a BIAS of -0.008 and RMSE of ±0.03 for the atmospheric transmissivity. In terms of simulated LST, these errors yield a deviation of ±0.9 K when applying a single-channel method

Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling

Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global change) and FLUXPEC (Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean “dehesa” ecosystem) projects LST retrieved from Landsat data is required to integrate ground-based observations of energy, water, and carbon fluxes with multi-scale remotely-sensed data and assess water and carbon balance in ecologically fragile heterogeneous ecosystem of Mediterranean wooded grassland (dehesa). Thus, three methods based on the Radiative Transfer Equation were used to extract LST from a series of 2009–2011 Landsat-5 TM images to assess the applicability for temperature input generation to a Landsat-MODIS LST integration. When compared to surface temperatures simulated using MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) with atmospheric profiles inputs (LSTref), values from Single-Channel (SC) algorithm are the closest (root-mean-square deviation (RMSD) = 0.50 °C); procedure based on the online Radiative Transfer Equation Atmospheric Correction Parameters Calculator (RTE-ACPC) shows RMSD = 0.85 °C; Mono-Window algorithm (MW) presents the highest RMSD (2.34 °C) with systematical LST underestimation (bias = 1.81 °C). Differences between Landsat-retrieved LST and MODIS LST are in the range of 2 to 4 °C and can be explained mainly by differences in observation geometry, emissivity, and time mismatch between Landsat and MODIS overpasses. There is a seasonal bias in Landsat-MODIS LST differences due to greater variations in surface emissivity and thermal contrasts between landcover components

Correção atmosférica de imagens termais utilizando perfis verticais de alta resolução simulados por um modelo de mesoescala

A estimativa da temperatura da superfície terrestre ( LST ) por sensoriamento remoto no infravermelho termal (TIR) é dependente d a realização de uma correção atmosférica apropriada que , em geral, necessita de perfis atmosféricos como dados de entrada. Dados globais de reanálise são uma alternativa prática para a obtenção desses perfis, mas podem apresentar limitações. Nesse contexto, o presente estudo teve como objetivo analisar a utilização do modelo numérico Weather Research and Forecasting (WRF) para gerar perfis verticais de alta resolução , refinando dados de reanálise , visando a correção atmosférica no TIR para o cálculo de valores de LST. Para tal, foram realizadas simulações com o modelo WRF com dados de reanálise do NCEP Climate Forecast System Version 2 (CFSv2) como condições iniciais e utilizando duas grades aninhadas com resoluções horizontais de 12 km (G12) e 3 km (G03). Para estimar a LST, foram empregados: o método da inversão direta da Equação de Transferência Radiativa (RTE) , o modelo MODTRAN e valores de radiância da banda 10 do Landsat 8 TIRS. A pesquisa avaliou o desempenho do modelo através dos perfis verticais, dos parâmetros atmosféricos de correção (transmitância atmosférica e radiâncias upwelling e downwelling ) e dos valores de LST, utilizando como referência dados de radiossondagens in situ , no sul do Brasil . Adicionalmente, foi executada uma análise de sensibilidade a dois esquemas de parametrização de camada limite planetária . Os resultados indicam que o modelo WRF simula de maneira satisfatória os perfis atmosféricos que, por consequência, geram parâmetros de correção e LST com baixos erros. Contudo, não existe melhora significativa nas métricas estatísticas entre os perfis extraídos diretamente da reanálise CFSv2 e os simulados pelo WRF . Em alguns casos, a utilização de um perfil de grade mais refinada resultou, até mesmo, em maiores erros. Os valores gerais de RMSE para a LST foram: 0,55 K ( CFSv2), 0,79 K ( WRF G12 ) e 0,82 K ( WRF G03 ). A escolha do esquema de camada limite mostrou - se caso - dependente. Conclui - se que não há necessidade especial de refinar a resolução dos perfis de reanálise visando estimativa de LST, por meio do método da RTE .The Land Surface Temperature (LST) retrieval from thermal infrared (TIR) remote sensing depends on performing an appropriate atmospheric correction. In general, this approach requires atmospheric profiles as input data. Global reanalysis data are a practical alternative to obtain these profiles, but they may have limitations. In this con text, this study aimed to assess the use of the Weather Research and Forecasting (WRF) numerical model to generate high - resolution vertical profiles, downscaling reanalysis data , to be applied in TIR atmospheric correction for LST retrieval . WRF simulations were carried out using NCEP Climate Forecast System Version 2 (CFSv2) reanalysis as initial conditions and two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03) . To retrieve the LST, we used: the Radiative Transfer Equation (RTE) based method , the MODTRAN model, and radiance values from Landsat 8 TIRS10 band . Th is research evaluated the model performance through vertical profiles, atmospheric correction parameters (atmospheric transmittance and upwelling and downwelling radiances) , and LST values, using in situ radiosonde data ( in Southern Brazil ) as reference. Moreover, a sensitivity analysis to two planetary boundary layer parameterization schemes was performed . The results indicate that the WRF model satisfactor il y simulates the atmospheric profiles that, consequently, generate correction param eters and LST with low errors. However, there is no significant improvement in statistical metrics between profiles extracted directly from the CFSv2 reanalysis and those simulated by WRF . In some cases, the use of a finer grid profile resulted even in larger errors. The LST overall RMSE values were: 0.55 K (CFSv2), 0.79 K (WRF G12) , and 0.82 K (WRF G03) . The boundary layer scheme choice proved to be case - dependent. We concluded that there is no special need to increase the resolution of reanalysis profiles in order to retrieve LST using the RTE - based method

Avaliação de métodos single channel na estimativa da temperatura da superfície terrestre no hemisfério sul a partir de dados orbitais no infravermelho termal

Diversos métodos tem sido propostos para estimar a Temperatura da Superfície Terrestre (LST) a partir de dados do infravermelho termal obtidos por satélites. Esses esforços são uma tentativa de minimizar os erros impostos na solução não-linear da transferência radiativa no sistema superfície-atmosfera. Além disso, a correção atmosférica em dados termais é um dos fatores fundamentais para obter LST acurada. Os métodos Single Channel (SC) permitem derivar a LST a partir da radiância medida em uma banda. Assim, consistem em uma oportunidade para estimar LST de longo prazo com os dados termais da série Landsat, com quase 40 anos de registro. Contudo, a maioria dos SC é desenvolvida e projetada para as condições atmosféricas e de superfície do Hemisfério Norte. Nesse contexto, essa dissertação objetivou avaliar o desempenho dos algoritmos Generalized Single Channel (GSC), Improved Single Channel (ISC) e Surface Temperature product (produto ST) na estimativa da LST em uma região litorânea do Hemisfério Sul. Para tanto, um campo de dunas composto por 99,53% de quartzo foi selecionado e dados do Landsat 8 TIRS foram utilizados. Inicialmente, para fundamentar a avaliação, investigou-se a aplicabilidade de perfis verticais de diferentes resoluções espaciais e horizontais, derivados de produtos de reanálise NCEP, na correção atmosférica, bem como, os impactos gerados na estimativa da LST. Os resultados mostraram que os perfis NCEP CFSv2 de resoluções originais e os perfis NCEP FNL utilizados pela Calculadora de Parâmetros de Correção Atmosférica (ACPC) da NASA foram os mais adequados para a correção atmosférica. Considerando as vantagens da ACPC, ela também foi utilizada para estimar os dados atmosféricos empregados nos algoritmos GSC e ISC na avaliação. O algoritmo ISC (RMSE de 0,69 K) apresentou o melhor desempenho na estimativa da LST, seguido do GSC (RMSE de 2,5 K) e do produto ST (RMSE de 4,24 K). De modo geral, concluiu-se que o ISC se mostrou o mais adequado para calcular a LST, podendo ser aplicado em estudos de balanço de energia, onde um erro de até 2 K é aceitável. Confirmou-se, portanto, a importância da análise dos métodos SC no presente trabalho, justificada pela sua aplicação em dados de radiância de sensores termais com uma banda, principalmente para estudos com séries temporais de LST da série Landsat, além de situações de mau funcionamento de canais espectrais.Several methods have been proposed to estimate the Land Surface Temperature (LST) from thermal infrared data obtained by satellites. These efforts are an attempt to minimize errors imposed in the nonlinear solution of radiative transfer in the surface- atmosphere system. Furthermore, atmospheric correction in thermal data is one of the key factors to obtain accurate LST. Single Channel (SC) methods allow to retrieve the LST from the measured radiance in one band. Thus, they provide an opportunity to estimate long-term LST with the Landsat thermal data series, with almost 40 years of record. However, most SCs are developed and designed for the atmospheric and surface conditions of the Northern Hemisphere. In this context, this dissertation aimed to evaluate the performance of the Generalized Single Channel (GSC), Improved Single Channel (ISC) and Surface Temperature product (ST product) algorithms in estimating LST in a coastal region of the Southern Hemisphere. For that, a dune field composed of 99.53% quartz was selected and Landsat 8 TIRS data were used. Initially, to support the evaluation, the applicability of vertical profiles of different spatial and horizontal resolutions, derived from NCEP reanalysis products, in atmospheric correction, as well as the impacts generated in the LST estimation, was investigated. The results showed that the original resolution NCEP CFSv2 profiles and the NCEP FNL profiles used by NASA's Atmospheric Correction Parameters Calculator (ACPC) were the most suitable for atmospheric correction. Considering the advantages of ACPC, it was also used to estimate the atmospheric data used in the GSC and ISC algorithms in the evaluation. The ISC algorithm (RMSE of 0.69 K) presented the best performance in retrieving the LST, followed by the GSC (RMSE of 2.5 K) and the ST product (RMSE of 4.24 K). In general, it was concluded that the ISC proved to be the most suitable to calculate the LST, and can be applied in energy balance studies, where an error of up to 2 K is acceptable. Therefore, the importance of the analysis of SC methods in the present work was confirmed, justified by their application in radiance data from thermal sensors with one band, mainly for studies with time series of LST from Landsat series, in addition to situations of channel malfunction

Land Surface Temperature (LST) estimated from Landsat images: applications in burnt areas and tree-grass woodlands (dehesas)

A lo largo de los últimos 40 años, las diferentes misiones del proyecto Landsat han proporcionado una gran cantidad de información espectral sobre la superficie terrestre. Las imágenes obtenidas por estos satélites se caracterizan por una resolución espacial de tipo medio, bandas espectrales situadas en diferentes regiones del espectro electromagnético (ópticas y térmicas) y una amplia cobertura terrestre. Si bien las bandas del óptico han sido utilizadas con éxito en numerosas aplicaciones, el uso del térmico ha sido mucho más limitado, a pesar de la gran importancia que representa el parámetro de la temperatura de superficie para numerosas aplicaciones ambientales, especialmente para aquellas relacionadas con la modelización de los flujos de energía en el sistema suelo-vegetación-atmósfera y con el cambio global. En este contexto, el objetivo principal de la presente investigación es explorar el potencial de la temperatura de superficie terrestre (siglas en inglés - LST), derivada de imágenes Landsat, en el estudio de ecosistemas heterogéneos, concretamente (i) áreas afectadas por los incendios forestales y (ii) ecosistemas de dehesa,formaciones constituidas por los árboles dispersos y pastizal/cultivos. En primer lugar, en el marco del proyecto BIOSPEC “Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of Global Change” (http://www.lineas.cchs.csic.es/biospec) se comparan las diferentes metodologías disponibles para la estimación de la LST a partir de la banda térmica de Landsat. Los mejores resultados, en condiciones atmosféricas caracterizadas por niveles medios de contenido de vapor, se obtuvieron usando el método mono-banda (en inglés - SingleChannel) (Jiménez-Muñoz et al., 2009), con un error de estimación <1º K. En el siguiente paso de la investigación la información sobre la distribución de LST derivada del sensor Thematic Mapper se utilizó en el análisis de la severidad del fuego en una zona forestal de Las Hurdes(Extremadura, España), y en el estudio de los efectos ocasionados por los diferentes tratamientos post-incendio en una zona quemada, esta vez localizada en los Montes de Zuera (Zaragoza, España). En relación con la severidad del fuego analizada en diferentes fechas post-incendio, se han detectado diferencias estadísticamente significativas entre los valores de LST correspondientes a las categorías de severidad establecidas a partir del índice espectral ΔNBR (Key y Benson, 2006).Los niveles de LST más elevados se observaron en las zonas donde la severidad del fuego fue mayor, debido a la menor emisividad de los productos de combustión y los cambios en el balance de energía relacionados con la ausencia de vegetación. En cuanto a las consecuencias de los tratamientos de madera quemada en la regeneración vegetal, se han observado diferencias estadísticamente significativas entre las áreas intervenidas y no intervenidas. En este sentido, en las áreas no intervenidas se registraron valores de LST ~1 K más bajos y niveles de recubrimiento vegetal ~10% más altos que en las intervenidas. En otro ámbito de aplicación, los datos de LST obtenidos mediante imágenes de Landsat-5 TM (período 2009-2011), se utilizaron en el análisis de los patrones espacio-temporales de la LST y su relación con el grado de ocupación de la fracción arbórea en ecosistemas de dehesa. Se ha detectado una relación negativa entre la LST y la cobertura arbórea, con diferencias a nivel estacional debido al dinamismo del ciclo fenológico del pastizal

Flaring and pollution detection in the Niger Delta using Remote Sensing

Merged with duplicate record 10026.1/6553 on 28.02.2017 by CS (TIS)Abstract Through the Global Gas Flaring Reduction (GGFR) initiative a substantial amount of effort and international attention has been focused on the reduction of gas flaring since 2002 (Elvidge et al., 2009). Nigeria is rated as the second country in the world for gas flaring, after Russia. In an attempt to reduce and eliminate gas flaring the federal government of Nigeria has implemented a number of gas flaring reduction projects, but poor governmental regulatory policies have been mostly unsuccessful in phasing it out. This study examines the effects of pollution from gas flaring using multiple satellite based sensors (Landsat 5 TM and Landsat 7 ETM+) with a focus on vegetation health in the Niger Delta. Over 131 flaring sites in all 9 states (Abia, Akwa Ibom, Bayelsa, Cross Rivers, Delta, Edo, Imo, Ondo and Rivers) of the Niger Delta region have been identified, out of which 11 sites in Rivers State were examined using a case study approach. Land Surface Temperature data were derived using a novel procedure drawing in visible band information to mask out clouds and identify appropriate emissivity values for different land cover types. In 2503 out of 3001 Landsat subscenes analysed, Land Surface Temperature was elevated by at least 1 ℃ within 450 m of the flare. The results from fieldwork, carried out at the Eleme Refinery II Petroleum Company and Onne Flow Station, are compared to the Landsat 5 TM and Landsat 7 ETM+ data. Results indicate that Landsat data can detect gas flares and their associated pollution on vegetation health with acceptable accuracy for both Land Surface Temperature (range: 0.120 to 1.907 K) and Normalized Differential Vegetation Index (sd ± 0.004). Available environmental factors such as size of facility, height of stack, and time were considered. Finally, the assessment of the impact of pollution on a time series analysis (1984 to 2013) of vegetation health shows a decrease in NDVI annually within 120 m from the flare and that the spatio-temporal variability of NDVI for each site is influenced by local factors. This research demonstrated that only 5 % of the variability in δLST and only 12 % of the variability in δNDVI, with distance from the flare stack, could be accounted for by the available variables considered in this study. This suggests that other missing factors (the gas flaring volume and vegetation speciation) play a significant role in the variability in δLST and δNDVI respectively

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

Remote Sensing Applications to Support Locust Management and Research: Evaluating the Potential of Earth Observation for Locust Outbreaks in Different Regions

This dissertation focuses on satellite remote sensing applications for locust management and additional contributions to locust research. Specifically, the remote sensing-based characterization and interpretation of land surface cover and its dynamics are addressed with a special emphasis on the requirements of different locust species. At first, the aim of this dissertation is to provide a holistic overview of the existing applications using satellite data focusing on different locust species and thus, to present current and new opportunities. Furthermore, remote sensing and geospatial datasets are used in a model to categorize areas with ideal and less than ideal conditions for locust outbreaks. The benefit of up-to-date remote sensing data for preventive locust management is demonstrated using time-series-based Sentinel-2 land cover classification. Due to the diversity of the numerous locust species and their spatial distribution in different geographical locations, this research focuses mainly on two locust species, the Italian locust (Calliptamus italicus) and the Moroccan locust (Dociostaurus maroccanus), as well as on selected study areas within their extensive habitats, respectively. Both selected locust species caused numerous damages in Europe, the Caucasus, Central Asia and North Africa in the past. For both species, there is only a limited number of publications exploiting the capabilities of remote sensing methods. Therefore, this dissertation aims to explore the potential approaches of Earth observation datasets to support preventive locust management and research for both species.Die vorliegende Dissertation beschäftigt sich mit dem Einsatz der Satellitenfernerkundung im Bereich Heuschreckenmanagement und -forschung. Die fernerkundungsbasierte Charakterisierung und Interpretation der Landoberflächen-bedeckung und deren Dynamik stehen dabei - mit Fokus auf die Anforderungen der verschiedenen Heuschreckenarten - im Vordergrund. Ziel dieser Dissertation ist es zunächst, einen ganzheitlichen Überblick über vorhandene Anwendungen von Satellitendaten im Kontext Heuschreckenmanagement zu erarbeiten. Des Weiteren werden fernerkundungs- und geobasierten Datensätzen in einem Model verwendet, um Flächen mit idealen bzw. weniger idealen Bedingungen für Heuschreckenausbrüche zu kategorisieren. Der Vorteil von aktuellen Fernerkundungsdaten für präventives Heuschreckenmanagement wird anhand zeitreihenbasierten Sentinel-2 Landbedeckungsklassifikation demonstriert. Aufgrund der Vielfältigkeit der zahlreichen Heuschreckenarten und deren räumlicher Verteilung in verschiedenen geographischen Lagen, konzentriert sich diese Arbeit im Wesentlichen auf zwei Heuschreckenarten, die Italienische Schönschrecke (Calliptamus italicus) und die Marokkanische Wanderheuschrecke (Dociostaurus maroccanus), sowie auf ausgewählte Studiengebiete innerhalb deren weiträumigen Habitaten. Beide Heuschreckenarten verursachten zahlreiche Ausbrüche in der Vergangenheit mit Schäden in Europa, dem Kaukasus, Zentralasien und Nordafrika. Für beide Heuschreckenarten existieren nur wenige Forschungsarbeiten, die sich mit der Anwendung von Fern-erkundungsdaten auseinandersetzen. Vor diesem Hintergrund zielt diese Dissertation auf die Entwicklung von relevanten Methoden unter Einsatz von Fernerkundungsdaten für beide Heuschreckenarten ab, um präventives Heuschreckenmanagement und -forschung zu unterstützen.Данная диссертация раскрывает тему применения спутникового дистанционного зондирования для контроля саранчовых и проведения дополнительных исследований саранчи. В частности, особое внимание уделяется изучению потребностей различных видов саранчовых при описании характеристик земного покрова и его динамики на основе данных дистанционного зондирования. Первостепенная цель данной диссертации состоит в том, чтобы предоставить целостный обзор существующих приложений, использующих спутниковые данные, в разрезе различных видов саранчовых для того, чтобы раскрыть текущие и потенциальные возможности. Кроме того, дистанционное зондирование и наборы геопространственных данных используются для классификации территорий с идеальными и не идеальными условиями для нашествий саранчи. исследование сосредоточено в основном на двух видах саранчи, итальянского пруса (Calliptamus italicus) и марокканской саранче (Dociostaurus maroccanus), а также на определенных территориях, в пределах их обширногo местообитаний

CHARACTERIZING RICE RESIDUE BURNING AND ASSOCIATED EMISSIONS IN VIETNAM USING A REMOTE SENSING AND FIELD-BASED APPROACH

Agricultural residue burning, practiced in croplands throughout the world, adversely impacts public health and regional air quality. Monitoring and quantifying agricultural residue burning with remote sensing alone is difficult due to lack of field data, hazy conditions obstructing satellite remote sensing imagery, small field sizes, and active field management. This dissertation highlights the uncertainties, discrepancies, and underestimation of agricultural residue burning emissions in a small-holder agriculturalist region, while also developing methods for improved bottom-up quantification of residue burning and associated emissions impacts, by employing a field and remote sensing-based approach. The underestimation in biomass burning emissions from rice residue, the fibrous plant material left in the field after harvest and subjected to burning, represents the starting point for this research, which is conducted in a small-holder agricultural landscape of Vietnam. This dissertation quantifies improved bottom-up air pollution emissions estimates through refinements to each component of the fine-particulate matter emissions equation, including the use of synthetic aperture radar timeseries to explore rice land area variation between different datasets and for date of burn estimates, development of a new field method to estimate both rice straw and stubble biomass, and also improvements to emissions quantification through the use of burning practice specific emission factors and combustion factors. Moreover, the relative contribution of residue burning emissions to combustion sources was quantified, demonstrating emissions are higher than previously estimated, increasing the importance for mitigation. The dissertation further explored air pollution impacts from rice residue burning in Hanoi, Vietnam through trajectory modelling and synoptic meteorology patterns, as well as timeseries of satellite air pollution and reanalysis datasets. The results highlight the inherent difficulty to capture air pollution impacts in the region, especially attributed to cloud cover obstructing optical satellite observations of episodic biomass burning. Overall, this dissertation found that a prominent satellite-based emissions dataset vastly underestimates emissions from rice residue burning. Recommendations for future work highlight the importance for these datasets to account for crop and burning practice specific emission factors for improved emissions estimates, which are useful to more accurately highlight the importance of reducing emissions from residue burning to alleviate air quality issues