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

Sharpening ECOSTRESS and VIIRS Land Surface Temperature Using Harmonized Landsat-Sentinel Surface Reflectances

Land surface temperature (LST) is a key diagnostic indicator of agricultural water use and crop stress. LST data retrieved from thermal infrared (TIR) band imagery, however, tend to have a coarser spatial resolution (e.g., 100 m for Landsat 8) than surface reflectance (SR) data collected from shortwave bands on the same instrument (e.g., 30 m for Landsat). Spatial sharpening of LST data using the higher resolution multi-band SR data provides an important path for improved agricultural monitoring at sub-field scales. A previously developed Data Mining Sharpener (DMS) approach has shown great potential in the sharpening of Landsat LST using Landsat SR data co-collected over various landscapes. This work evaluates DMS performance for sharpening ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST (~70 m native resolution) and Visible Infrared Imaging Radiometer Suite (VIIRS) LST (375 m) data using Harmonized Landsat and Sentinel-2 (HLS) SR data, providing the basis for generating 30-m LST data at a higher temporal frequency than afforded by Landsat alone. To account for the misalignment between ECOSTRESS/VIIRS and Landsat/HLS caused by errors in registration and orthorectification, we propose a modified version of the DMS approach that employs a relaxed box size for energy conservation (EC). Sharpening experiments were conducted over three study sites in California, and results were evaluated visually and quantitatively against LST data from unmanned aerial vehicles (UAV) flights and from Landsat 8. Over the three sites, the modified DMS technique showed improved sharpening accuracy over the standard DMS for both ECOSTRESS and VIIRS, suggesting the effectiveness of relaxing EC box in relieving misalignment-induced errors. To achieve reasonable accuracy while minimizing loss of spatial detail due to the EC box size increase, an optimal EC box size of 180–270 m was identified for ECOSTRESS and about 780 m for VIIRS data based on experiments from the three sites. Results from this work will facilitate the development of a prototype system that generates high spatiotemporal resolution LST products for improved agricultural water use monitoring by synthesizing multi-source remote sensing data

A Review on Different Modeling Techniques

In this study, the importance of air temperature from different aspects (e.g., human and plant health, ecological and environmental processes, urban planning, and modelling) is presented in detail, and the major factors affecting air temperature in urban areas are introduced. Given the importance of air temperature, and the necessity of developing high-resolution spatio- temporal air-temperature maps, this paper categorizes the existing approaches for air temperature estimation into three categories (interpolation, regression and simulation approaches) and reviews them. This paper focuses on high-resolution air temperature mapping in urban areas, which is difficult due to strong spatio-temporal variations. Different air temperature mapping approaches have been applied to an urban area (Berlin, Germany) and the results are presented and discussed. This review paper presents the advantages, limitations and shortcomings of each approach in its original form. In addition, the feasibility of utilizing each approach for air temperature modelling in urban areas was investigated. Studies into the elimination of the limitations and shortcomings of each approach are presented, and the potential of developed techniques to address each limitation is discussed. Based upon previous studies and developments, the interpolation, regression and coupled simulation techniques show potential for spatio-temporal modelling of air temperature in urban areas. However, some of the shortcomings and limitations for development of high-resolution spatio- temporal maps in urban areas have not been properly addressed yet. Hence, some further studies into the elimination of remaining limitations, and improvement of current approaches to high-resolution spatio-temporal mapping of air temperature, are introduced as future research opportunities

Towards an operational model for estimating day and night instantaneous near-surface air temperature for urban heat island studies: outline and assessment

Near-surface air temperature (NSAT) is key for assessing urban heat islands, human health, and well-being. However, a widely recognized and cost- and time-effective replicable approach for estimating hourly NSAT is still urgent. In this study, we outline and validate an easy-to-replicate, yet effective, operational model, for automating the estimation of high-resolution day and night instantaneous NSAT. The model is tested on a heat wave event and for a large geographical area. The model combines remotely sensed land surface temperature and digital elevation model, with air temperature from local fixed weather station networks. Achieved NSAT has daily and hourly frequency consistent with MODIS revisiting time. A geographically weighted regression method is employed, with exponential weighting found to be highly accurate for our purpose. A robust assessment of different methods, at different time slots, both day- and night-time, and during a heatwave event, is provided based on a cross-validation protocol. Four-time periods are modelled and tested, for two consecutive days, i.e. 31st of July 2020 at 10:40 and 21:50, and 1st of August 2020 at 02:00 and 13:10 local time. High R2 was found for all time slots, ranging from 0.82 to 0.88, with a bias close to 0, RMSE ranging from 1.45 °C to 1.77 °C, and MAE from 1.15 °C to 1.36 °C. Normalized RMSE and MAE are roughly 0.05 to 0.08. Overall, if compared to other recognized regression models, higher effectiveness is allowed also in terms of spatial autocorrelation of residuals, as well as in terms of model sensitivity

Estimation of All-Weather 1 km MODIS Land Surface Temperature for Humid Summer Days

Land surface temperature (LST) is used as a critical indicator for various environmental issues because it links land surface fluxes with the surface atmosphere. Moderate-resolution imaging spectroradiometers (MODIS) 1 km LSTs have been widely utilized but have the serious limitation of not being provided under cloudy weather conditions. In this study, we propose two schemes to estimate all-weather 1 km Aqua MODIS daytime (1:30 p.m.) and nighttime (1:30 a.m.) LSTs in South Korea for humid summer days. Scheme 1 (S1) is a two-step approach that first estimates 10 km LSTs and then conducts the spatial downscaling of LSTs from 10 km to 1 km. Scheme 2 (S2), a one-step algorithm, directly estimates the 1 km all-weather LSTs. Eight advanced microwave scanning radiometer 2 (AMSR2) brightness temperatures, three MODIS-based annual cycle parameters, and six auxiliary variables were used for the LST estimation based on random forest machine learning. To confirm the effectiveness of each scheme, we have performed different validation experiments using clear-sky MODIS LSTs. Moreover, we have validated all-weather LSTs using bias-corrected LSTs from 10 in situ stations. In clear-sky daytime, the performance of S2 was better than S1. However, in cloudy sky daytime, S1 simulated low LSTs better than S2, with an average root mean squared error (RMSE) of 2.6 degrees C compared to an average RMSE of 3.8 degrees C over 10 stations. At nighttime, S1 and S2 demonstrated no significant difference in performance both under clear and cloudy sky conditions. When the two schemes were combined, the proposed all-weather LSTs resulted in an average R-2 of 0.82 and 0.74 and with RMSE of 2.5 degrees C and 1.4 degrees C for daytime and nighttime, respectively, compared to the in situ data. This paper demonstrates the ability of the two different schemes to produce all-weather dynamic LSTs. The strategy proposed in this study can improve the applicability of LSTs in a variety of research and practical fields, particularly for areas that are very frequently covered with clouds

Geographical information systems and remote sensing methods in the estimation of potential dew volume and its utilization in the United Arab Emirates

In a fast growing region of Middle East and with rapid depletion of fossil groundwater, possibilities for dew utilization as a limited renewable water resource play an important role in the water management of the United Arab Emirates. Despite projected changes in air temperature and rainfall, geographical and topographical features of the UAE show some potential for dew harvesting, mostly at the altitudes higher than 1000 m and some isolated oasis areas. With the help of geographical information system (GIS), remote sensing, and numerical and theoretical methods, approximate volumes of dew were estimated. Meteorological data was inputted together with theoretical and numerical calculations into grids by using pixelization processes. Methods such as zonal statistics, kriging, semi-kriging, and interpolation were implemented through GIS software. Another method used in this research is supervised classification and normalized difference vegetation index (NDVI) which is being determined by means of software IDRISI terra set. After finishing all the proposed methods applied in this research, four belts of potential dew use were presented. The Arabian Desert in the territory of the United Arab Emirates has no potential for dew utilization. The zone close to the oases has very low possibility of dew use. The hilly-mountainous area between 500 and 800 m.a.s.l. has medium possibility for dew use. There is a high possibility for dew use on mountain Al Hajar, occupying the area higher than 800 m; 1.3% of the whole country’s territory has excellent potential for dew use. In this part of the country, theoretically, it is possible to use dew for farming and partial watering. Experimental study together with GIS, remote sensing, and numerical analysis may extend knowledge about dew properties. Although this research includes theoretical calculations of dew utilization and has some limitations, it still presents a new insight into climate cycles in this part of the Arabian Peninsula and a way to understand them better

Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data

Urbanisation generates greater population densities and an increase in anthropogenic heat generation. These factors elevate the urban�rural air temperature (Ta) difference, thus generating the Urban Heat Island (UHI) phenomenon. Ta is used in the fields of public health and epidemiology to quantify deaths attributable to heat in cities around the world: the presence of UHI can exacerbate exposure to high temperatures during summer periods, thereby increasing the risk of heat-related mortality. Measuring and monitoring the spatial patterns of Ta in urban contexts is challenging due to the lack of a good network of weather stations. This study aims to produce a parsimonious model to retrieve maximum Ta (Tmax) at high spatio-temporal resolution using Earth Observation (EO) satellite data. The novelty of this work is twofold: (i) it will produce daily estimations of Tmax for London at 1 km2 during the summertime between 2006 and 2017 using advanced statistical techniques and satellite-derived predictors, and (ii) it will investigate for the first time the predictive power of the gradient boosting algorithm to estimate Tmax for an urban area. In this work, 6 regression models were calibrated with 6 satellite products, 3 geospatial features, and 29 meteorological stations. Stepwise linear regression was applied to create 9 groups of predictors, which were trained and tested on each regression method. This study demonstrates the potential of machine learning algorithms to predict Tmax: the gradient boosting model with a group of five predictors (land surface temperature, Julian day, normalised difference vegetation index, digital elevation model, solar zenith angle) was the regression model with the best performance (R² = 0.68, MAE = 1.60 °C, and RMSE = 2.03 °C). This methodological approach is capable of being replicated in other UK cities, benefiting national heat-related mortality assessments since the data (provided by NASA and the UK Met Office) and programming languages (Python) sources are free and open. This study provides a framework to produce a high spatio-temporal resolution of Tmax, assisting public health researchers to improve the estimation of mortality attributable to high temperatures. In addition, the research contributes to practice and policy-making by enhancing the understanding of the locations where mortality rates may increase due to heat. Therefore, it enables a more informed decision-making process towards the prioritisation of actions to mitigate heat-related mortality amongst the vulnerable population

Development of Geospatial Models for Multi-Criteria Decision Making in Traffic Environmental Impacts of Heavy Vehicle Freight Transportation

Heavy vehicle freight transportation is one of the primary contributors to the socio-economic development, but it has great influence on traffic environment. To comprehensively and more accurately quantify the impacts of heavy vehicles on road infrastructure performance, a series of geospatial models are developed for both geographically global and local assessment of the impacts. The outcomes are applied in flexible multi-criteria decision making for the industrial practice of road maintenance and management

Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE 2 AT Center study protocol

Introduction: African cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand the complexities of heat-related health impacts in these cities. The objectives are: (1) mapping intraurban heat risk and exposure using health, socioeconomic, climate and satellite imagery data; (2) creating a stratified heat–health forecast model to predict adverse health outcomes; and (3) establishing an early warning system for timely heatwave alerts. The ultimate goal is to foster climate-resilient African cities, protecting disproportionately affected populations from heat hazards. Methods and analysis: The research will acquire health-related datasets from eligible adult clinical trials or cohort studies conducted in Johannesburg and Abidjan between 2000 and 2022. Additional data will be collected, including socioeconomic, climate datasets and satellite imagery. These resources will aid in mapping heat hazards and quantifying heat–health exposure, the extent of elevated risk and morbidity. Outcomes will be determined using advanced data analysis methods, including statistical evaluation, machine learning and deep learning techniques. Ethics and dissemination: The study has been approved by the Wits Human Research Ethics Committee (reference no: 220606). Data management will follow approved procedures. The results will be disseminated through workshops, community forums, conferences and publications. Data deposition and curation plans will be established in line with ethical and safety considerations