Downscaling landsat land surface temperature over the urban area of Florence

A new downscaling algorithm for land surface temperature (LST) images retrieved from Landsat Thematic Mapper (TM) was developed over the city of Florence and the results assessed against a high-resolution aerial image. The Landsat TM thermal band has a spatial resolution of 120 m, resampled at 30 m by the US Geological Survey (USGS) agency, whilst the airborne ground spatial resolution was 1 m. Substantial differences between Landsat USGS and airborne thermal data were observed on a 30 m grid: therefore a new statistical downscaling method at 30 m was developed. The overall root mean square error with respect to aircraft data improved from 3.3 °C (USGS) to 3.0 °C with the new method, that also showed better results with respect to other regressive downscaling techniques frequently used in literature. Such improvements can be ascribed to the selection of independent variables capable of representing the heterogeneous urban landscape

Determination of land surface temperature using Landsat 8 images: Comparative study of algorithms on the city of Granada

[EN] The use of satellite images has become, in recent decades, one of the most common ways to determine the Land Surface Temperature (LST). One of them is through the use of Landsat 8 images that requires the use of single-channel (MC) and two-channel (BC) algorithms. In this study, the LST of a medium-sized city, Granada (Spain) has been determined over a year by using five Landsat 8 algorithms that are subsequently compared with ambient temperatures. Few studies compare the data source with the seasonal variations of the same metropolis, which together with its geographical location, high pollution and the significant thermal variations it experiences make it a suitable place for the development of this research. As a result of the statistical analysis process, the regression coefficients R2, mean square error (RMSE), mean error bias (MBE) and standard deviation (SD) were obtained. The average results obtained reveal that the LST derived from the BC algorithms (1.0 °C) are the closest to the ambient temperatures in contrast to the MC (-5.6 °C), although important variations have been verified between the different zones of the city according to its coverage and seasonal periods. Therefore, it is concluded that the BC algorithms are the most suitable for recovering the LST of the city under study.[ES] El empleo de imágenes satelitales se ha convertido, en las últimas décadas, en una de las formas más habituales para determinar la Temperatura de la Superficie Terrestre (TST). Una de ellas es mediante el empleo de imágenes Landsat 8 que requiere del uso de algoritmos del tipo monocanal (MC) y bicanal (BC). En este estudio se ha determinado la TST de una ciudad de tamaño medio, Granada (España) a lo largo de un año mediante el empleo de cinco algoritmos Landsat 8 que posteriormente se comparan con las temperaturas ambientales. Pocos estudios comparan la fuente de datos con las variaciones estacio-temporales de una misma metrópolis lo que unido a su situación geográfica, alta contaminación y las importantes variaciones térmicas que experimenta la convierten en un lugar adecuado para el desarrollo de esta investigación. Como resultado del proceso de análisis estadístico se obtuvieron los coeficientes de regresión R2, el error medio cuadrático (RMSE), sesgo medio del error (MBE) y la desviación estándar (DE). Los resultados medios obtenidos revelan que las TST derivada de los algoritmos BC (1,0 °C) son las más próximas a las temperaturas ambientales en contraposición con los MC (-5,6 °C) aunque se han verificado importantes variaciones entre las distintas zonas de la urbe según su cobertura y los periodos estacionales. Por todo ello, se concluye que los algoritmos BC son los más adecuados para recuperar la TST de la urbe objeto de estudio.Hidalgo-García, D. (2021). 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The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

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

Retrieval of Land Surface Temperature of Lahore Through Landsat-8 TIRS Data

Land surface temperature (LST) is an important parameter in global climate change and urban thermalenvironmental studies. The significance of land surface temperature is being acknowledged gradually and interest isincreasing in developing methodologies for the retrieval of LST from Satellite Remote Sensing (SRS) data. ThermalInfrared Sensor (TIRS) of Landsat-8 is the newest TIR sensor for the Landsat Data Continuity Mission (LDCM),offering two adjacent thermal infrared bands (10, 11), having significant beneficiary for the land surface temperatureinversion. The spectral radiance can be estimated through TIR bands 10 and 11 of Landsat-8 OLI_TIRS satellite image.In the present study, the radiative transfer equation-based method has been employed in estimating LST of Lahore andthe analysis demonstrated that estimated LST has the highest accuracy from the radiative transfer method through band10. Land Surface Emissivity (LSE) was derived with the aid of the NDVI’s threshold technique. The present studyresults show that as the built-up area increases and vegetation cover decreases in urban surface, they are linked toincrease in urban land surface temperature and conversely larger vegetation cover associated with lower urbantemperature. The output exposed that LST was high in built-up and barren land, whereas it was low in the area wherethere were more vegetation cover and water

An algorithm to retrieve Land Surface Temperature using Landsat-8 Dataset

Soil moisture, surface temperature, and vegetation are variables that play an important role in our environment which in turn increases the demand for accurate estimation of certain geophysical parameters such as weather, flooding, and land classification. However, for accurate Land Surface Temperature (LST) estimation, remotely sensed data of key environmental forms were considered and applied in this research. The goal of this study was to apply a suitable algorithm for LST estimation from the Landsat-8 dataset that gives a great accuracy when compared with in-situ observations.Spatial and temporal Landsat-8 data were acquired which provided the analytical structure for linking specific data successfully due to fine resolutions. The data were then applied to determine brightness temperatures, vegetation cover, and surface emissivity which demonstrated the effectiveness of the Split-Window Algorithm as an optimum method for LST retrieval from satellite.The results show temperature variation over a long period of time can be used in observing varying temperature values based on terrain i.e. High temperatures in fully built up areas and low temperatures in the well-vegetated regions. Finally, accurate LST estimation is important for land classification, energy budget estimations as well as agricultural production.Keywords: Emissivity, Landsat, Land Surface Temperature, Split-Window, Vegetatio

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

In this study, the split window (SW) method was applied for land surface temperature (LST) retrieval using Landsat 8 in two apple orchards (Glindow, Altlandsberg). Four images were acquired during high demand of irrigation water from July to August 2018. After pre-processing images, the normalized difference vegetation index (NDVI) and LST were calculated by red, NIR, and thermal bands. The results were validated by interpolated infrared thermometer (IRT) measurements using the inverse distance weighting (IDW) method. In the next step, the temperature vegetation index (TVDI) was calculated based on the trapezoidal NDVI/LST space to determine the water status of apple trees in the case studies. Results show good agreement between interpolated LST using IRT measurements and remotely sensed LST calculation using SW in all satellite overpasses, where the absolute mean error was between 0.08 to 4.00 K and root mean square error (RMSE) values ranged between 0.71 and 4.23 K. The TVDI spatial distribution indicated that the trees suffered from water stress on 7 and 23 July and 8 August 2018 in Glindow apple orchard with the mean value of 0.69, 0.57, and 0.73, whereas in the Altlandsberg orchard on 17 August, the irrigation system compensated the water deficit as indicated by the TVDI value of 0.34. Moreover, a negative correlation between TVDI and vegetation water content (VWC) with correlation coefficient (r) of −0.81 was observed. The corresponding r for LST and VWC was equal to −0.89, which shows the inverse relation between water status and temperature-based indices. The results indicate that the LST and/or TVDI calculation using the proposed methods can be effectively applied for monitoring tree water status and support irrigation management in orchards using Landsat 8 satellite images without requiring ground measurements

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Land Surface Temperature Patterns in the Urban Agglomeration of Krakow (Poland) Derived from Landsat-7/ETM+ Data

The aim of this study was to identify typical and specific features of land surface temperature (LST) distribution in the city of Krakow and its surroundings with the use of Landsat/ETM+ data. The paper contains a detailed description of the study area and technical properties of the Landsat program and data, as well as a complete methodology of LST retrieval. Retrieved LST records have been standardized in order to ensure comparability between satellite images acquired during different seasons. The method also enables identification of characteristic thermal regions, i.e. areas always colder and always warmer than a zonal mean LST value for Krakow. The research includes spatial analysis of the standardized LST with regard to different land cover types. Basic zonal statistics such as mean standardized LST and percentage share of hot and cold regions within 10 land cover types were calculated. GIS was used for automated data processing and mapping. The results confirmed the most obvious dependence of the LST on different land cover types. Some more factors influencing the LST were recognized on the basis of detailed investigation of the LST pattern in the urban agglomeration of Krakow. The factors are: emission of anthropogenic heat, insolation of the surfaces depending first of all on land relief and shape of buildings, seasonal changes of vegetation and weather conditions at the time of satellite image acquisition

A review of geothermal mapping techniques using remotely sensed data

Exploiting geothermal (GT) resources requires first and foremost locating suitable areas for its development. Remote sensing offers a synoptic capability of covering large areas in real time and can cost effectively explore prospective geothermal sites not easily detectable using conventional survey methods, thus can aid in the prefeasibility stages of geothermal exploration. In this paper, we evaluate the techniques and approaches used in literature for the detection of prospective geothermal sites. Observations have indicated that, while thermal temperature anomalies detection have been applicable in areas of magmatic episodes and volcanic activity, poor resolution especially from space borne data is still a challenge. Consequently, thermal anomalies have been detected with some degree of success using airborne data, however, this is mostly in locations of known surface manifestations such as hot springs and fumaroles. The indirect identification of indicator minerals related to geothermal systems have been applied using multispectral and hyperspectral data in many studies. However, the effectiveness of the techniques relies on the sophistication and innovative digital image processing methods employed to sieve out relevant spectral information. The use of algorithms to estimate land surface temperature and heat fluxes are also applied to aid thermal anomaly detection, nevertheless, remote sensing techniques are still complementary to geologic, geophysical and geochemical survey methods. While not the first of its kind, this review is aimed at identifying new developments, with a focus on the trends and limitations intrinsic to the techniques and a look at current gaps and prospects for the future.Keywords: Geothermal, remote sensing, thermal anomalies, indicator minerals, multispectral, hyperspectra