Urban energy exchanges monitoring from space

One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. However, satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (-64.1, +69.3 W m-2 for ±2 K perturbation); and also underestimate anthropogenic heat flux. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling

Urban storage heat flux variability explored using satellite, meteorological and geodata

The storage heat flux (ΔQS) is the net flow of heat stored within a volume that may include the air, trees, buildings and ground. Given the difficulty of measurement of this important and large flux in urban areas, we explore the use of Earth Observation (EO) data. EO surface temperatures are used with ground-based meteorological forcing, urban morphology, land cover and land use information to estimate spatial variations of ΔQS in urban areas using the Element Surface Temperature Method (ESTM). First, we evaluate ESTM for four “simpler” surfaces. These have good agreement with observed values. ESTM coupled to SUEWS (an urban land surface model) is applied to three European cities (Basel, Heraklion, London), allowing EO data to enhance the exploration of the spatial variability in ΔQS. The impervious surfaces (paved and buildings) contribute most to ΔQS. Building wall area seems to explain variation of ΔQS most consistently. As the paved fraction increases up to 0.4, there is a clear increase in ΔQS. With a larger paved fraction, the fraction of buildings and wall area is lower which reduces the high values of ΔQS

Anthropogenic Heat Flux Estimation from Space: Results of the first phase of the URBANFLUXES Project

H2020-Space project URBANFLUXES (URBan ANthrpogenic heat FLUX from Earth observation Satellites) investigates the potential of Copernicus Sentinels to retrieve anthropogenic heat flux, as a key component of the Urban Energy Budget (UEB). URBANFLUXES advances the current knowledge of the impacts of UEB fluxes on urban heat island and consequently on energy consumption in cities. This will lead to the development of tools and strategies to mitigate these effects, improving thermal comfort and energy efficiency. In URBANFLUXES, the anthropogenic heat flux is estimated as a residual of UEB. Therefore, the rest UEB components, namely, the net all-wave radiation, the net change in heat storage and the turbulent sensible and latent heat fluxes are independently estimated from Earth Observation (EO), whereas the advection term is included in the error of the anthropogenic heat flux estimation from the UEB closure. The project exploits Sentinels observations, which provide improved data quality, coverage and revisit times and increase the value of EO data for scientific work and future emerging applications. These observations can reveal novel scientific insights for the detection and monitoring of the spatial distribution of the urban energy budget fluxes in cities, thereby generating new EO opportunities. URBANFLUXES thus exploits the European capacity for space-borne observations to enable the development of operational services in the field of urban environmental monitoring and energy efficiency in cities. H2020-Space project URBANFLUXES (URBan ANthrpogenic heat FLUX from Earth observation Satellites)investigates the potential of Copernicus Sentinels to retrieve anthropogenic heat flux, as a key component of the UrbanEnergy Budget (UEB). URBANFLUXES advances the current knowledge of the impacts of UEB fluxes on urban heatisland and consequently on energy consumption in cities. This will lead to the development of tools and strategies tomitigate these effects, improving thermal comfort and energy efficiency. In URBANFLUXES, the anthropogenic heatflux is estimated as a residual of UEB. Therefore, the rest UEB components, namely, the net all-wave radiation, the netchange in heat storage and the turbulent sensible and latent heat fluxes are independently estimated from EarthObservation (EO), whereas the advection term is included in the error of the anthropogenic heat flux estimation from theUEB closure. The project exploits Sentinels observations, which provide improved data quality, coverage and revisittimes and increase the value of EO data for scientific work and future emerging applications. These observations canreveal novel scientific insights for the detection and monitoring of the spatial distribution of the urban energy budgetfluxes in cities, thereby generating new EO opportunities. URBANFLUXES thus exploits the European capacity forspace-borne observations to enable the development of operational services in the field of urban environmentalmonitoring and energy efficiency in cities

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort

Copernicus for urban resilience in Europe

The urban community faces a significant obstacle in effectively utilising Earth Observation (EO) intelligence, particularly the Copernicus EO program of the European Union, to address the multifaceted aspects of urban sustainability and bolster urban resilience in the face of climate change challenges. In this context, here we present the efforts of the CURE project, which received funding under the European Union’s Horizon 2020 Research and Innovation Framework Programme, to leverage the Copernicus Core Services (CCS) in supporting urban resilience. CURE provides spatially disaggregated environmental intelligence at a local scale, demonstrating that CCS can facilitate urban planning and management strategies to improve the resilience of cities. With a strong emphasis on stakeholder engagement, CURE has identified eleven cross-cutting applications between CCS that correspond to the major dimensions of urban sustainability and align with user needs. These applications have been integrated into a cloud-based platform known as DIAS (Data and Information Access Services), which is capable of delivering reliable, usable and relevant intelligence to support the development of downstream services towards enhancing resilience planning of cities throughout Europe

Earth observation-based operational estimation of soil moisture and evapotranspiration for agricultural crops in support of sustainable water management

Global information on the spatio-temporal variation of parameters driving the Earth’s terrestrial water and energy cycles, such as evapotranspiration (ET) rates and surface soil moisture (SSM), is of key significance. The water and energy cycles underpin global food and water security and need to be fully understood as the climate changes. In the last few decades, Earth Observation (EO) technology has played an increasingly important role in determining both ET and SSM. This paper reviews the state of the art in the use specifically of operational EO of both ET and SSM estimates. We discuss the key technical and operational considerations to derive accurate estimates of those parameters from space. The review suggests significant progress has been made in the recent years in retrieving ET and SSM operationally; yet, further work is required to optimize parameter accuracy and to improve the operational capability of services developed using EO data. Emerging applications on which ET/SSM operational products may be included in the context specifically in relation to agriculture are also highlighted; the operational use of those operational products in such applications remains to be seen

Monitoring the impact of land cover change on surface urban heat island through google earth engine. Proposal of a global methodology, first applications and problems

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to "Make cities inclusive, safe, resilient and sustainable". In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992-2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities

Variability of urban surface temperatures and implications for aerodynamic energy exchange in unstable conditions

Sensible heat flux (QH) is a critical driver of surface and boundary layer meteorological processes, especially in urban areas. Aerodynamic resistance methods (ARM) to model QH are promising because, in principle, all that is needed is surface temperature (T0), air temperature (TA) and an aerodynamic resistance term (rH). There are significant challenges in urban areas however, due to uncertainties in satellite-derived land surface temperatures (LST), logistical challenges to obtain high-resolution air temperatures, and limited understanding of spatial and temporal variability of rH and associated variables (e.g. thermal roughness length). This work uses an extensive LST dataset covering six years (2011-2016) in central London and a long-term in situ observation network to analyse variability of LST and rH variables. Results show that LST is spatially correlated with building and vegetation land cover with coherent thermal structures at length scales less than 500-1000 m. Additionally, satellite-observed LST varies with average building height (up to 10% cooler in areas with tall buildings). The rH term and associated variables are observed to vary on daily and seasonal cycles and findings are used to model QH using five variations of an ARM-based approach on a 100 m pixel basis. Modelled QH is compared to observations from three scintillometer paths and an eddy covariance flux tower. We find generally good agreement between observations and models, though there is uncertainty in all methods (mean absolute error ranges from 58.1-129.3 W m-2) due to challenges in determining high-resolution meteorological and surface inputs, particularly LST and friction velocity (u*). Additional complexity in evaluating modelled QH arises from anthropogenic heat sources: long-term tower-based observations show that TA and radiometer-derived T0 are warmer during working weekdays than non-working days (up to 0.7C) and that there is an observed lag (2-3 hours) between energy consumption and observed warming and QH

Mapping evapotranspiration variability over a complex oasis-desert ecosystem based on automated calibration of Landsat 7 ETM+ data in SEBAL

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature T-s and Normalized Difference Vegetation Index NDVI (). Results were comprehensively validated with flux components and actual ET (ETa) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: - desert ecosystem ETa R-2 = 0.94; oasis ecosystem ETa R-2 = 0.98 (P < 0.05). The improvement uncovered the exact land use contributions to ETa variability, with average estimates ranging from 1.24 mm to 6.98 mm . Additionally, instantaneous ET to NDVI (ETins-NDVI) ratio indicated that desert and oasis consumptive water use vary significantly with time of the season. This study indicates the possibility of continuous daily ET monitoring with considerable implications for improving water resources decision support over complex data-scarce drylands