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

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

Landsat Surface Temperature Product: Global Validation and Uncertainty Estimation

Surface temperature is an important Earth system data record that is useful to fields such as change detection, climate research, environmental monitoring, and many smaller scale applications like agriculture. Earth-observing satellites can be used to derive this metric, with the goal that a global product can be established. There are a series of Landsat satellites designed for this purpose, whose data archives provides the longest running source of continuously acquired multispectral imagery. The moderate spatial and temporal resolution, in addition to its well calibrated sensors and data archive make Landsat an unparalleled and attractive choice for many research applications. Through the support of the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS), a Landsat Surface Temperature product (LST) has been developed. Currently, it has been validated for Landsat 5 scenes in North America, and Landsat 7 on a global scale. Transmission and cloud proximity were used to characterize LST error for various conditions, which showed that 30% of the validation data had root mean squared errors (RMSEs) less than 1 K, and 62% had RMSEs less than 2 K. Transmission and cloud proximity were also used to develop a LST uncertainty estimation method, which will allow the user to choose data points that meet their accuracy requirements. For the same dataset, about 20% reported LST uncertainties less than 1 K, and 63% had uncertainties less than 2 K. Enabling global validation and establishing an uncertainty estimation method were crucially important achievements for the LST product, which is now ready to be implemented and scaled so that it is available to the public. This document will describe the LST algorithm in full, and it will also discuss the validation results and uncertainty estimation process

Repairing Landsat Satellite Imagery Using Deep Machine Learning Techniques

Satellite Imagery is one of the most widely used sources to analyze geographic features and environments in the world. The data gathered from satellites are used to quantify many vital problems facing our society, such as the impact of natural disasters, shore erosion, rising water levels, and urban growth rates. In this paper, we construct machine learning and deep learning algorithms for repairing anomalies in the Landsat satellite imagery data which arise for various reasons ranging from cloud obstruction to satellite malfunctions. The accuracy of GIS data is crucial to ensuring the models produced from such data are as close to reality as possible. Reducing the inherent bias caused by the obstruction or obfuscation of reflectance values is a simple but effective way to more closely represent the reality of our environment with satellite data. Using clean pixels from previously acquired satellite imagery, we were able to model the bias present in each scene at different times and apply algorithms to fix the inconsistencies. The machine learning model decreased the mean absolute error by an average of 80.1% compared to traditional repair algorithms such as mosaicking

Land Surface Temperature Product Validation Best Practice Protocol Version 1.0 - October, 2017

The Global Climate Observing System (GCOS) has specified the need to systematically generate andvalidate Land Surface Temperature (LST) products. This document provides recommendations on goodpractices for the validation of LST products. Internationally accepted definitions of LST, emissivity andassociated quantities are provided to ensure the compatibility across products and reference data sets. Asurvey of current validation capabilities indicates that progress is being made in terms of up-scaling and insitu measurement methods, but there is insufficient standardization with respect to performing andreporting statistically robust comparisons.Four LST validation approaches are identified: (1) Ground-based validation, which involvescomparisons with LST obtained from ground-based radiance measurements; (2) Scene-based intercomparisonof current satellite LST products with a heritage LST products; (3) Radiance-based validation,which is based on radiative transfer calculations for known atmospheric profiles and land surface emissivity;(4) Time series comparisons, which are particularly useful for detecting problems that can occur during aninstrument's life, e.g. calibration drift or unrealistic outliers due to undetected clouds. Finally, the need foran open access facility for performing LST product validation as well as accessing reference LST datasets isidentified

Εκτίμηση Εδαφικής Υγρασίας από Πολυφασματικά και Ραντάρ Δορυφορικά Δεδομένα με χρήση του Google Earth Engine και Τεχνικών Μηχανικής Μάθησης

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Γεωπληροφορική

Linking thermal variability and change to urban growth in Harare Metropolitan City using remotely sensed data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal. Pietermaritzburg, 2017.Urban growth, which involves Land Use and Land Cover Changes (LULCC), alters land surface thermal properties. Within the framework of rapid urban growth and global warming, land surface temperature (LST) and its elevation have potential significant socio-economic and environmental implications. Hence the main objectives of this study were to (i) map urban growth, (ii) link urban growth with indoor and outdoor thermal conditions and (iii) estimate implications of thermal trends on household energy consumption as well as predict future urban growth and temperature patterns in Harare Metropolitan, Zimbabwe. To achieve these objectives, broadband multi-spectral Landsat 5, 7 and 8, in-situ LULC observations, air temperature (Ta) and humidity data were integrated. LULC maps were obtained from multi-spectral remote sensing data and derived indices using the Support Vector Machine Algorithm, while LST were derived by applying single channel and split window algorithms. To improve remote sensing based urban growth mapping, a method of combining multi-spectral reflective data with thermal data and vegetation indices was tested. Vegetation indices were also combined with socio-demographic data to map the spatial distribution of heat vulnerability in Harare. Changes in outdoor human thermal discomfort in response to seasonal LULCC were evaluated, using the Discomfort Index (DI) derived parsimoniously from LST retrieved from Landsat 8 data. Responses of LST to long term urban growth were analysed for the period from 1984 to 2015. The implications of urban growth induced temperature changes on household air-conditioning energy demand were analysed using Landsat derived land surface temperature based Degree Days. Finally, the Cellular Automata Markov Chain (CAMC) analysis was used to predict future landscape transformation at 10-year time steps from 2015 to 2045. Results showed high overall accuracy of 89.33% and kappa index above 0.86 obtained, using Landsat 8 bands and indices. Similar results were observed when indices were used as stand-alone dataset (above 80%). Landsat 8 derived bio-physical surface properties and socio-demographic factors, showed that heat vulnerability was high in over 40% in densely built-up areas with low-income when compared to “leafy” suburbs. A strong spatial correlation (α = 0.61) between heat vulnerability and surface temperatures in the hot season was obtained, implying that LST is a good indicator of heat vulnerability in the area. LST based discomfort assessment approach retrieved DI with high accuracy as indicated by mean percentage error of less than 20% for each sub-season. Outdoor thermal discomfort was high in hot dry season (mean DI of 31oC), while the post rainy season was the most comfortable (mean DI of 19.9oC). During the hot season, thermal discomfort was very low in low density residential areas, which are characterised by forests and well maintained parks (DI ≤27oC). Long term changes results showed that high density residential areas increased by 92% between 1984 and 2016 at the expense of cooler green-spaces, which decreased by 75.5%, translating to a 1.98oC mean surface temperature increase. Due to surface alterations from urban growth between 1984 and 2015, LST increased by an average of 2.26oC and 4.10oC in the cool and hot season, respectively. This decreased potential indoor heating energy needed in the cool season by 1 degree day and increased indoor cooling energy during the hot season by 3 degree days. Spatial analysis showed that during the hot season, actual energy consumption was low in high temperature zones. This coincided with areas occupied by low income strata indicating that they do not afford as much energy and air conditioning facilities as expected. Besides quantifying and strongly relating with energy requirement, degree days provided a quantitative measure of heat vulnerability in Harare. Testing vegetation indices for predictive power showed that the Urban Index (UI) was comparatively the best predictor of future urban surface temperature (r = 0.98). The mean absolute percentage error of the UI derived temperature was 5.27% when tested against temperature derived from thermal band in October 2015. Using UI as predictor variable in CAMC analysis, we predicted that the low surface temperature class (18-28oC) will decrease in coverage, while the high temperature category (36-45oC) will increase in proportion covered from 42.5 to 58% of city, indicating further warming as the city continues to grow between 2015 and 2040. Overall, the findings of this study showed that LST, human thermal comfort and air-conditioning energy demand are strongly affected by seasonal and urban growth induced land cover changes. It can be observed that urban greenery and wetlands play a significant role of reducing LST and heat transfer between the surface and lower atmosphere and LST may continue unless effective mitigation strategies, such as effective vegetation cover spatial configuration are adopted. Limitations to the study included inadequate spatial and low temporal resolution of Landsat data, few in-situ observations of temperature and LULC classification which was area specific thus difficult for global comparison. Recommendations for future studies included data merging to improve spatial and temporal representation of remote sensing data, resource mobilization to increase urban weather station density and image classification into local climate zones which are of easy global interpretation and comparison

機械学習を用いた空間ダウンスケールの気温予測法及び都市ヒートアイランドへの応用に関する研究

This study introduced a temperature spatial downscaling method based on machine learning algorithm to downscale air temperature from 1 km to 250 m for high-resolution atmosphere urban heat island (UHI) analysis. The core of this downscaling method is to establish the regression model between urban structure and temperature, and then we used the unchanged characteristics of regression models at different scale to predict high-resolution temperature data with high-resolution resolution urban structure, thereby analyzed atmosphere urban heat island. Finally, we compared the similarity and differentiation between atmosphere UHI and surface UHI. The results indicated the following: (1) The machine learning method was proved to be suitable for the air temperature spatial downscaling predication; (2) The UHI characteristics of metropolitan areas in different climatic regions of Japan are different; (3) There are great differences in intensity and spatial distribution between atmosphere UHI and surface UHI is great.北九州市立大