Spatial and Multi-Temporal Analysis of Land Surface Temperature through Landsat 8 Images: Comparison of Algorithms in a Highly Polluted City (Granada)

Over the past decade, satellite imaging has become a habitual way to determine the land surface temperature (LST). One means entails the use of Landsat 8 images, for which mono window (MW), single channel (SC) and split window (SW) algorithms are needed. Knowing the precision and seasonal variability of the LST can improve urban climate alteration studies, which ultimately help make sustainable decisions in terms of the greater resilience of cities. In this study we determine the LST of a mid-sized city, Granada (Spain), applying six Landsat 8 algorithms that are validated using ambient temperatures. In addition to having a unique geographical location, this city has high pollution and high daily temperature variations, so that it is a very appropriate site for study. Altogether, 11 images with very low cloudiness were taken into account, distributed between November 2019 and October 2020. After data validation by means of R2 statistical analysis, the root mean square error (RMSE), mean bias error (MBE) and standard deviation (SD) were determined to obtain the coefficients of correlation. Panel data analysis is presented as a novel element with respect to the methods usually used. Results reveal that the SC algorithms prove more effective and reliable in determining the LST of the city studied here.ERDF (European Rural Development Fund)Ministry of Science and Innovation (State Research Agency) EQC2018-004702-

Determining ground-level composition and concentration of particulate matter across regional areas using the Himawari-8 satellite

Speciated ground-level aerosol concentrations are required to understand and mitigate health impacts from dust storms, wildfires and other aerosol emissions. Globally, surface monitoring is limited due to cost and infrastructure demands. While remote sensing can help estimate respirable (i.e. ground level) concentrations, current observations are restricted by inadequate spatiotemporal resolution, uncertainty in aerosol type, particle size, and vertical profile. One key issue with current remote sensing datasets is that they are derived from reflectances observed by polar orbiting imagers, which means that aerosol is only derived during the daytime, and only once or twice per day. Sub-hourly, infrared (IR), geostationary data, such as the ten-minute data from Himawari-8, are required to monitor these events to ensure that sporadic dust events can be continually observed and quantified. Newer quantification methods using geostationary data have focussed on detecting the presence, or absence, of a dust event. However, limited attention has been paid to the determination of composition, and particle size, using IR wavelengths exclusively. More appropriate IR methods are required to quantify and classify aerosol composition in order to improve the understanding of source impacts. The primary research objectives were investigated through a series of scientific papers centred on aspects deemed critical to successfully determining ground-level concentrations. A literature review of surface particulate monitoring of dust events using geostationary satellite remote sensing was undertaken to understand the theory and limitations in the current methodology. The review identified (amongst other findings) the reliance on visible wavelengths and the lack of temporal resolution in polar-orbiting satellite data. As a result of this, a duststorm was investigated to determine how rapidly the storm passed and what temporal data resolution is required to monitor these and other similar events. Various IR dust indices were investigated to determine which are optimum for determining spectral change. These indices were then used to qualify and quantitate dust events, and the methodology was validated against three severe air quality events of a dust storm; smoke from prescribed burns; and an ozone smog incident. The study identified that continuous geostationary temporal resolution is critical in the determination of concentration. The Himawari-8 spatial resolution of 2 km is slightly coarse and further spatial aggregation or cloud masking would be detrimental to determining concentrations. Five dual-band BTD combinations, using all IR wavelengths, maximises the identification of compositional differences, atmospheric stability, and cloud cover and this improves the estimated accuracy. Preliminary validation suggests that atmospheric stability, cloud height, relative humidity, PM2.5, PM10, NO, NO2, and O3 appear to produce plausible plumes but that aerosol speciation (soil, sea-spray, fires, vehicles, and secondary sulfates) and SO2 require further investigation. The research described in the thesis details the processes adopted for the development and implementation of an integrated approach to using geostationary remote sensing data to quantify population exposure (who), qualify the concentration and composition (what), assess the temporal (when) and spatial (where) concentration distributions, to determine the source (why) of aerosols contribution to resulting ground-level concentration

Land surface temperature and evapotranspiration estimation in the Amazon evergreen forests using remote sensing data

Amazonian tropical forests play a significant role in global water, carbon and energy cycles. Considering the relevance of this biome and the climate change projections which predict a hotter and drier climate for the region, the monitoring of the vegetation status of these forests becomes of significant importance. In this context, vegetation temperature and evapotranspiration (ET) can be considered as key variables. Vegetation temperature is directly linked with plant physiology. In addition, some studies have shown the existing relationship between this variable and the CO2 absorption capacity and biomass loss of these forests. Evapotranspiration resulting from the combined processes of transpiration and evaporation links the terrestrial water, carbon and surface energy exchanges of these forests. How this variable will response to the changing climate is critical to understand the stability of these forests. Satellite remote sensing is presented as a feasible means in order to provide accurate spatially-distributed estimates of these variables. Nevertheless, the use of satellite passive imagery for analysing this region still has some limitations being of special importance the proper cloud masking of the satellite data which becomes a difficult task due to the continuous cloud cover of the region. Under the light of the aforementioned issues, the present doctoral thesis is aimed at estimating the land surface temperature and evapotranspiration of the Amazonian tropical forests using remote sensing data. In addition, as cloud screening of satellite imagery is a critical step in the processing chain of the previous magnitudes and becomes of special importance for the study region this topic has also been included in this thesis. We have mainly focused on the use of data from the Moderate Resolution Imaging Spectroradiometer (MODIS) which is amongst major tools for studying this region. Regarding the cloud detection topic, the potential of supervised learning algorithms for cloud masking was studied in order to overcome the cloud contamination issue of the current satellite products. Models considered were: Gaussian Naïve Bayes (GNB), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forests (RF), Support Vector Machine (SVM) and Multilayer Perceptron (MLP). These algorithms are able to provide a continuous measure of cloud masking uncertainty (i.e. a probability estimate of each pixel belonging to clear and cloudy class) and therefore can be used under the light of a probabilistic approach. Reference dataset (a priori knowledge) requirement was satisfied by considering the collocation of Cloud Profiling Radar (CPR) and Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) observations with MODIS sensor. Model performance was tested using three independent datasets: 1) collocated CPR/CALIOP and MODIS data, 2) MODIS manually classified images and 3) in-situ ground data. For the case of satellite image and in-situ testing, results were additionally compared to current operative MYD35 (version 6.1) and Multi-Angle Implementation of the Atmospheric Correction (MAIAC) cloud masking algorithms. These results showed that machine learning algorithms were able to improve MODIS operative cloud masking performance over the region. MYD35 and MAIAC tended to underestimate and overestimate the cloud cover, respectively. Amongst the models considered, LDA stood out as the best candidate because of its maximum accuracy (difference in Kappa coefficient of 0.293/0.155 (MYD35 /MAIAC respectively)) and minimum computational associated. Regarding the estimation of land surface temperature (LST), the aim of this study was to generate specific LST products for the Amazonian tropical forests. This goal was accomplished by using a tuned split-window (SW) equation. Validation of the LST products was obtained by direct comparison between LST estimates as derived from the algorithms and two types of different LST observations: in-situ LST (T-based validation) and LST derived from the R-based method. In addition, LST algorithms were validated using independent simulated data. In-situ LST was retrieved from two infrared radiometers (SI-100 and IR-120) and a CNR4 net radiometer, situated at Tambopata test site (12.832 S, 62.282 W) in the Peruvian Amazon. Apart from this, current satellite LST products were also validated and compared to the tuned split-window. Although we have mainly focus on MODIS LST products which derive from three different LST algorithms: split-window, day and night (DN) and Temperature Emissivity Separation (TES), we have also considered the inclusion of the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor. In addition, a first assessment of the Sea and Land Surface Temperature Radiometer (SLSTR) is presented. Validation was performed separately for daytime and nighttime conditions. For MODIS sensor, current LST products showed Root Mean Square Errors (RMSE) in LST estimations between 2 K and 3K for daytime and 1 K and 2 K for nighttime. In the best case (with a restrictive cloud screening) RMSE errors decrease to values below 2K and around 1 K, respectively. The proposed LST showed RMSE values of 1K to approximately 2 K and 0.7-1.5 K (below 1.5 K and below 1 K in the best case) for daytime and nighttime conditions, thus improving current LST MODIS products. This is also in agreement with the R-based validation results, which show a RMSE reduction of 0.7 K to 1.7 K in comparison to MODIS LST products. For the case of VIIRS sensor daytime conditions, VIIRS-TES algorithm provides the best performance with a difference of 0.2 K to around 0.3 K in RMSE regarding the split window algorithm (in the best case it reduces to 0.2 K). All VIIRS LST products considered have RMSE values between 2 K and 3K. At nighttime, however VIIRS-TES is not able to outperform the SW algorithm. A difference of 0.7 K to 0.8 K in RMSE is obtained. Contrary to MODIS and the SW LST products, VIIRS-TES tends to overestimate in-situ LST values. Regarding SLSTR sensor, the L2 product provides a better agreement with in-situ observations than the proposed algorithm (daytime difference in RMSE around 0.6 K and up 0.07 K at nighttime). In the estimation of the ET, we focused on the evaluation of four commonly used remote-sensing based ET models. These were: i) Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), ii) Penman-Monteith MODIS operative parametrization (PM-Mu), iii) Surface Energy Balance System (SEBS), and iv) Satellite Application Facility on Land Surface Analysis (LSASAF). These models were forced using remote-sensing data from MODIS and two ancillary meteorological data sources: i) in-situ data extracted from Large-Scale Biosphere-Atmosphere Experiment (LBA) stations (scenario I), and ii) three reanalysis datasets (scenario II), including Modern-Era Retrospective analysis for Research and Application (MERRA-2), European Centre for Medium-range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERA-Interim), and Global Land Assimilation System (GLDAS-2.1). Performance of algorithms under the two scenarios was validated using in-situ eddy-covariance measurements. For scenario I, PT-JPL provided the best agreement with in-situ ET observations (RMSE = 0.55 mm/day, R = 0.88). Neglecting water canopy evaporation resulted in an underestimation of ET measurements for LSASAF. SEBS performance was similar to that of PT-JPL, nevertheless SEBS estimates were limited by the continuous cloud cover of the region. A physically-based ET gap-filling method was used in order to alleviate this issue. PM-Mu also with a similar performance to PT-JPL tended to overestimate in-situ ET observations. For scenario II, quality assessment of reanalysis input data demonstrated that MERRA-2, ERA-Interim and GLDAS-2.1 contain biases that impact model performance. In particular, biases in radiation inputs were found the main responsible of the observed biases in ET estimates. For the region, MERRA-2 tends to overestimate daily net radiation and incoming solar radiation. ERA-Interim tends to underestimate both variables, and GLDAS-2.1 tends to overestimate daily radiation while underestimating incoming solar radiation. Discrepancies amongst these inputs resulted in large absolute deviations in spatial patterns (deviations greater than 500 mm/year) and temporal patterns

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Remote Sensing of the Aquatic Environments

The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

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

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing