scholarly journals Parameterizing Anthropogenic Heat Flux with an Energy-Consumption Inventory and Multi-Source Remote Sensing Data

2017 ◽  
Vol 9 (11) ◽  
pp. 1165 ◽  
Author(s):  
◽  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Energy Consumption ◽  
Remote Sensing Data ◽  
Anthropogenic Heat ◽  
Sensing Data ◽  
Anthropogenic Heat Flux

scholarly journals Anthropogenic Heat Flux Estimation Based on Luojia 1-01 New Nighttime Light Data: A Case Study of Jiangsu Province, China

2020 ◽  
Vol 12 (22) ◽  
pp. 3707
Author(s):  
Zhongli Lin ◽  
Hanqiu Xu
Keyword(s):  
Heat Flux ◽  
Energy Consumption ◽  
Urban Climate ◽  
Power Estimation ◽  
Anthropogenic Heat ◽  
Economic Activities ◽  
Estimation Model ◽  
Nighttime Light Data ◽  
Anthropogenic Heat Flux ◽  
Nighttime Light

With the rapid process of urbanization, anthropogenic heat generated by human activities has become an important factor that drives the changes in urban climate and regional environmental quality. The nighttime light (NTL) data can aptly reflect the spatial distribution of social-economic activities and energy consumption, and quantitatively estimate the anthropogenic heat flux (AHF) distribution. However, the commonly used DMSP/OLS and Suomi-NPP/VIIRS NTL data are restricted by their coarse spatial resolution and, therefore, cannot exhibit the spatial details of AHF at city scale. The 130 m high-resolution NTL data obtained by Luojia 1-01 satellite launched in June 2018 shows a promise to solve this problem. In this paper, the gridded AHF spatial estimation is achieved with a resolution of 130 m using Luojia 1-01 NTL data based on three indexes, NTLnor (Normalized Nighttime Light Data), HSI (Human Settlement Index), and VANUI (Vegetation Adjusted NTL Urban Index). We chose Jiangsu, a fast-developing province in China, as an example to determine the best AHF estimation model among the three indexes. The AHF of 96 county-level cities of the province was first calculated using energy-consumption statistics data and then correlated with the corresponding data of three indexes. The results show that based on a 5-fold cross-validation approach, the VANUI power estimation model achieves the highest R2 of 0.8444 along with the smallest RMSE of 4.8277 W·m−2 and therefore has the highest accuracy among the three indexes. According to the VANUI power estimation model, the annual mean AHF of Jiangsu in 2018 was 2.91 W·m−2. Of the 96 cities, Suzhou has the highest annual mean AHF of 7.41 W·m−2, followed by Wuxi, Nanjing, Changzhou and Zhenjiang, with the annual mean of 3.80–5.97 W·m−2, while the figures of Suqian, Yancheng, Lianyungang, and Huaian, the cities in northern Jiangsu, are relatively low, ranging from 1.41 to 1.59 W·m−2. This study has shown that the AHF estimation model developed by Luojia 1-01 NTL data can achieve higher accuracy at city-scale and discriminate the spatial detail of AHF effectively.

Latent heat flux estimation in clear sky days over Indian agroecosystems using noontime satellite remote sensing data

2009 ◽  
Vol 149 (10) ◽  
pp. 1646-1665 ◽  
Author(s):  
Kaniska Mallick ◽  
Bimal K. Bhattacharya ◽  
V.U.M. Rao ◽  
D. Raji Reddy ◽  
Saon Banerjee ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Clear Sky ◽  
Heat Flux Estimation ◽  
Flux Estimation

scholarly journals Revising the definition of anthropogenic heat flux from buildings: role of human activities and building storage heat flux

2021 ◽  
Author(s):  
Yiqing Liu ◽  
Zhiwen Luo ◽  
Sue Grimmond
Keyword(s):  
Heat Flux ◽  
Energy Consumption ◽  
Energy Balance ◽  
Human Activities ◽  
Building Energy ◽  
Heat Emission ◽  
Heat Fluxes ◽  
Anthropogenic Heat ◽  
Anthropogenic Heat Flux ◽  
The Difference

Abstract. Buildings are a major source of anthropogenic heat emissions, impacting energy use and human health in cities. The difference between building energy consumption and building anthropogenic heat emission magnitudes and time lag and are poorly quantified. Energy consumption (QEC) is a widely used proxy for the anthropogenic heat flux from buildings (QF,B). Here we revisit the latter’s definition. If QF,B is the heat emission to the outdoor environment from human activities within buildings, we can derive it from the changes in energy balance fluxes between occupied and unoccupied buildings. Our derivation shows the difference between QEC and QF,B is attributable to a change in the storage heat flux induced by human activities (∆So-uo) (i.e., QF,B = QEC − ∆So-uo). Using building energy simulations (EnergyPlus) we calculate the energy balance fluxes for a simplified isolated building (obtaining QF,B, QEC, ∆So-uo) with different occupancy states. The non-negligible differences in diurnal patterns between QF,B and QEC caused by thermal storage (e.g. hourly QF,B to QEC ratios vary between −2.72 and 5.13 within a year in Beijing, China). Negative QF,B can occur as human activities can reduce heat emission from building but are associated with a large storage heat flux. Building operations (e.g., open windows, use of HVAC system) modify the QF,B by affecting not only QEC but also the ∆So-uo diurnal profile. Air temperature and solar radiation are critical meteorological factors explaining day-to-day variability of QF,B. Our new approach could be used to provide data for future parameterisations of both anthropogenic heat flux and storage heat fluxes from buildings. It is evident that storage heat fluxes in cities may also be impacted by occupant behaviour.

scholarly journals Assessment of actual evapotranspiration over a semi-arid heterogeneous land surface by means of coupled low resolution remote sensing data with energy balance model: comparison to extra Large Aperture Scintillometer measurements

2017 ◽  
Author(s):  
Sameh Saadi ◽  
Gilles Boulet ◽  
Malik Bahir ◽  
Aurore Brut ◽  
Bernard Mougenot ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Water Stress ◽  
Latent Heat ◽  
Water Availability ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Sparse Model ◽  
Sensing Data ◽  
Semi Arid

Abstract. In semi-arid areas, agricultural production is restricted by water availability; hence efficient agricultural water management is a major issue. The design of tools providing regional estimates of evapotranspiration (ET), one of the most relevant water balance fluxes, may help the sustainable management of water resources. Remote sensing provides periodic data about actual vegetation temporal dynamics (through the Normalized Difference Vegetation Index NDVI) and water availability under water stress (through the land surface temperature LST) which are crucial factors controlling ET. In this study, spatially distributed estimates of ET (or its energy equivalent, the latent heat fluxes LE) in the Kairouan plain (Central Tunisia) were computed by applying the Soil Plant Atmosphere and Remote Sensing Evapotraspiration (SPARSE) model fed by low resolution remote sensing data (Terra and Aqua MODIS). The work goal was to assess the operational use of the SPARSE model and the accuracy of the modelled i) sensible heat flux (H) and ii) daily ET over a heterogeneous semi-arid landscape with a complex land cover (i.e. trees, winter cereals, summer vegetables). The SPARSE's layer approach was run to compute instantaneous estimates of H and LE fluxes at the satellite overpass time. The good correspondence (R2 = 0.60 and 0.63 and RMSE = 57.89 W/m-2 and 53.85 W/m-2; for Terra and Aqua, respectively) between instantaneous H estimates and large aperture scintillometer (XLAS)'s H measurements along a pathlength of 4 km over the study area showed that the SPARSE model presents satisfactory accuracy. Results showed that, despite the fairly large scatter, the instantaneous LE can be suitably estimated at large scale (RMSE = 47.20 W/m-2 and 43.20 W/m-2; for Terra and Aqua, respectively and R2 = 0.55 for both satellites). Additionally, water stress was investigated by comparing modelled (SPARSE derived) to observed (XLAS derived) water stress values; we found that most points were located within a 0.2 confidence interval, thus the general tendencies are well reproduced. Even though extrapolation of instantaneous latent heat flux values to daily totals was less obvious, daily ET estimates are deemed acceptable.

scholarly journals Estimation of Terrestrial Latent Heat Flux Based on Chinese GaoFen-1 Remote Sensing Data

2021 ◽  
Author(s):  
Ruiyang Yu ◽  
Yunjun Yao ◽  
Ke Shang ◽  
Junming Yang ◽  
Xiaozheng Guo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Mapping Latent Heat Flux in the Western Forest Covered Regions of Algeria Using Remote Sensing Data and a Spatialized Model

2009 ◽  
Vol 1 (4) ◽  
pp. 795-817 ◽  
Author(s):  
Souidi Zahira ◽  
Hamimed Abderrahmane ◽  
Khalladi Mederbal ◽  
Donze Frederic
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Remote Sensing Data ◽  
Sensing Data
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