Intraurban Analysis of Surface Urban Heat Island From Disagregated Thermal Radiance Images

Surface Urban Heat Islands (SUHI) are areas with higher surface temperatures than their surroundings. Several studies have used thermal images from satellites to research the influence of urbanization on surface temperature patterns, however the low spatial resolution of thermal sensors limits the analysis of LST intraurban variations. Attempting to overcome this limitation, we used the Enhanced Physical Model (EPM) for disaggregation of land surface temperature (DLST) to generate fine scale LST for Sao Paulo city in Brazil. This method uses a linear regression and Planck’s law to combine NDVI, NDWI and UI to estimate LST at finer spatial detail. First, we calibrate the method by upscaling an ASTER thermal band to 1000 m and using EPM to estimate the original 100 m thermal band. The original and estimated ASTER thermal bands achieved and R² of 0.66. Following, we apply the EPM model to estimate the LST at 15 m and compare it with data from meteorological stations. The 15 m LST image facilitated the identification of potential SUHIs. The EPM model provides an enhanced product with higher level of spatial detail, which allows researchers to identify changes of surface temperature that would not be evident from an ASTER LST (90 m spatial resolution) product. In summary, the model allowed us to quantify and map the influence of different urbanization patterns on the LST distribution.Ilhas de calor de superfície (ICS)são áreas com temperature de superfície maior do que as áreas ao redor. Vários estudos tem usado imagens termais de satélite para investigar a influência da urbanização nos padrões de temperatura de superfície; entretanto a baixa resolução espacial dos atuais sensores termais limita a análise dos padrões de variação intraurbana de temperatura de superfície. Com o objetivo de surpassar essa limitação, nós utilizamos o the Enhanced Physical Model (EPM) para gerar dados de temperatura de superfície com maior nível de detalhamento para a cidade de São Paulo- Brasil. Esse método utiliza um modelo de regressão linear e a lei de Planck para combinar NDVI, NDWI e UI para estimar a temperatura de superfície com maior nível de detalhes espaciais.  Primeiro, para calibrar o modelo, nós reamostramos uma banda termal ASTER para 1000 m e utilizamos o método EPM para estimar a banda original de 100 m. A banda termal estimatada de 100 m atingiu um R2= 0.66 em relação a banda termal original. A seguir,  nós aplicamos o método EPM para estimar a temperatura de superfície à 15 m. A imagem de temperatura de superfície de 15 m facilitou a identificação de potenciais ilhas de calor de superfície. O modelo EPM fornece um produto com alto grau de detalhamento espacial, o que permite que pesquisadores identifiquem as mudanças de temperatura de superfície que não seriam evidentes na imagem  termal ASTER original (90 m de resolução espacial). Em suma, o modelo nos permitiu quantificar e mapear a influência de diferentes padrões de urbanização na distribuição dos padrões de temperatura de superfície

A Novel Method to Estimate Subpixel Temperature by Fusing Solar-Reflective and Thermal-Infrared Remote-Sensing Data With an Artificial Neural Network

Among the multisource data fusing methods, the potential advantages of remote sensing of solar-reflective visible and near-Infrared [(VNIR); 400-900 nm] data and thermal-infrared (TIR) data have not been fully mined. Usually, a linear unmixed method is used for the purpose, which results in low estimation accuracy of subpixel land-surface temperature (LST). In this paper, we propose a novel method to estimate subpixel LST. This approach uses the characteristics of high spatial-resolution advanced spaceborne thermal emission and reflection radiometer (ASTER) VNIR data and the low spatial-resolution TIR data simulated from ASTER temperature product to generate the high spatial-resolution temperature data at a subpixel scale. First, the land-surface parameters (e.g., leaf area index, normalized difference vegetation index (NDVI), soil water content index, and reflectance) were extracted from VNIR data and field measurements. Then, the extracted high resolution of land-surface parameters and the LST were simulated into coarse resolutions. Second, the genetic algorithm and self-organizing feature map artificial neural network (ANN) was utilized to create relationships between land-surface parameters and the corresponding LSTs separately for different land-cover types at coarse spatial-resolution scales. Finally, the ANN-trained relationships were applied in the estimation of subpixel temperatures (at high spatial resolution) from high spatial-resolution land-surface parameters. The two sets of data with different spatial resolutions were simulated using an aggregate resampling algorithm. Experimental results indicate that the accuracy with our method to estimate land-surface subpixel temperature is significantly higher than that with a traditional method that uses the NDVI as an input parameter, and the average error of subpixel temperature is decreased by 2-3 K with our method. This method is a simple and convenient approach to estimate subpixel LST from high spatial-te- - mporal resolution data quickly and effectively

Enjeux de la réduction d'échelle dans l'estimation par télédétection des déterminants climatiques

Ce travail s'inscrit dans le cadre de recherche sur les maladies vectorielles de Lyme et Virus du Nil au sein de l'Agence de Santé Publique du Canada (ASPC) ayant pour finalité d'évaluer et de cartographier les risques sanitaires associés à ces maladies infectieuses liées au climat aux échelles municipales, provinciales et fédérale. Dans ce contexte, cette recherche vise à démontrer la faisabilité, la pertinence et les enjeux de recourir aux méthodes de réduction d'échelle pour obtenir à une haute résolution spatio-temporelle (100/30 m et 1 jour) avec au plus des marges d'erreur de 2 unités, des déterminants climatiques et microclimatiques (DCMC) en milieu hétérogène du Canada. Un cadre méthodologique d'application des méthodes de réduction d'échelle, Random Forest Regression (RFR), Thermal sharpening (TsHARP), Pixel block intensity modulation (PBIM), a été proposé pour estimer la température de surface (LST) de MODIS 1000 m à 100/30 m. Des expérimentations basées sur cette approche ont été effectuées sur trois sites au Québec à différentes époques. Les résultats, spatialement représentatifs, ont été validés avec les températures de l'air et celles prises par de Landsat 08 avec des marges d'erreur autour de 2°C. L'analyse des résultats démontre la capacité effective des méthodes de réduction d'échelle à discriminer la LST dans l'espace. Toutefois, dans le contexte du projet de l'ASPC, ces résultats sont non concluants à 100/30 m en l'absence d'une plus-value significative au plan spatial de LST. Cette analyse a conduit à discuter des enjeux temporels, spatiaux, méthodologiques et de gestion de gros volumes de données en lien avec la réduction d'échelle dans le contexte du projet.This research is part of the Public Health Agency of Canada's (PHAC) research on Lyme and West Nile Virus vector-borne diseases, which aims to assess and map the health risks associated with these climate-related infectious diseases at the municipal, provincial and federal levels. In this context, this research aims to demonstrate the feasibility, relevance and challenges of using downscaling methods to obtain high spatial and temporal resolution (100/30 m and 1 day), with margins of error of no more than 2 units, of climatic and microclimatic determinants (CMDs) in a heterogeneous Canadian environment. A methodological framework for the application of downscaling methods, Random Forest Regression (RFR), Thermal sharpening (TsHARP), Pixel block intensity modulation (PBIM), has been proposed to estimate the surface temperature (LST) from MODIS 1000 m to 100/30 m. Experiments with our approach were carried out at three sites in Quebec at different times. The spatially representative results were validated with air and Landsat 08 temperatures with error margins around 2°C. The analysis of our results demonstrates the effective capacity of downscaling methods to discriminate LST in space. However, in the context of the ASPC project, these results are inconclusive at 100/30 m in the absence of a significant, expected increase in the spatial accuracy of LST. This analysis led to a discussion of the temporal, spatial, methodological and large data volume management issues related to downscaling in the context of the project