<p>The frequency of extreme heat related events is rising. This places the ever growing number of urban dwellers at higher risk. Quantifying these phenomena is important for the development and monitoring of climate change adaptation and mitigation policies. In this context, earth observations offer increasing opportunities to assess these phenomena with an unprecedented level of accuracy and spatial reach. Satellite thermal imaging systems acquire Land Surface Temperature (LST) which is fundamental to run models that study for example hotspots and heatwaves in urban environments.</p><p>Current instruments include TIRS on board Landsat 8 and MODIS on board of Terra satellites. These provide LST products on a monthly basis at 100m and twice per day at 1km respectively. Other sensors on board geostationary satellites, such as MSG and GOES-R, produce sub-hourly thermal images. For example the SEVIRI instrument onboard MSG, captures images every 15 minutes. However, this is done at an even coarser spatial resolution, which is 3 to 5 km in the case of SEVIRI. Nevertheless, none of the existing systems can capture LST synchronously with fine spatial resolution at a high temporal frequency, which is a prerequisite for monitoring heat stress in urban environments.</p><p>Combining LST time series of high temporal resolution (i.e. sub-daily MODIS- or SEVIRI-derived data) with products of fine spatial resolution (i.e. Landsat 8 products), and potentially other related variables (i.e. reflectance, spectral indices, land cover information, terrain parameters and local climatic variables), facilitates the downscaling of LST estimations. Nonetheless, considering the complexity of how distinct surfaces within a city heat-up differently during the course of a day, such a downscaling is meaningful for practically synchronous observations (e.g. Landsat-8 and MODIS Terra&#8217;s morning observations).</p><p>The recently launched ECOSTRESS mission provides multiple times in a day high spatial resolution thermal imagery at 70m. Albeit, recording the same locations on Earth every few days at varying times. We explore the associations between ECOSTRESS and Landsat-8 thermal data, based on the incoming radiation load and distinct surface properties characterised from other datasets. In our approach, first we upscale ECOSTRESS data to simulate Landsat-8 images at moments that coincide the acquisition times of other sensors products. In a second step, using the simulated Landsat-8 images, we downscale LST products acquired at later times, such as MODIS Aqua (ca. 13:30) or even the hourly MSG data. This composite downscaling procedure enables an enhanced LST estimation that opens the way for better diagnostics of the heat stress in urban landscapes.</p><p>In this study we discuss in detail the concepts of our approach and present preliminary results produced with the JEODPP, JRC's high throughput computing platform.</p>
A Simple Spatio–Temporal Data Fusion Method Based on Linear Regression Coefficient Compensation