Evaluating land surface albedo estimation from Landsat MSS, TM, ETM +, and OLI data based on the unified direct estimation approach

He, Tao; Liang, Shunlin; Wang, Dongdong; Cao, Yun‐Feng; Gao, Feng; Yu, Yunyue; Feng, Min

doi:10.1016/j.rse.2017.10.031

Cited by 99 publications

(62 citation statements)

References 57 publications

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“…physical and environmental factors. The factors that contribute to UHI effects have been reported in several previous studies 19,[38][39][40][41][42][43][44][45][46][47][48][49] . These factors can be grouped into two categories, physical and environmental.…”

Section: Methodsmentioning

confidence: 85%

Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea

Ngarambe

Duhirwe

et al. 2020

Sci Rep

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Urban heat island (UHi), a phenomenon involving increased air temperature of a city compared to the surrounding rural area, results in increased energy use and escalated health problems. to understand the magnitude and characteristics of UHi in Seoul and to accommodate for the high temporal variability and spatial heterogeneity of the UHi which make it inherently challenging to analyze using conventional statistical methods, we developed two deep learning models, a temporal UHi-model and a spatial UHi model, using a feed-forward deep neural network (Dnn) architecture. Data related to meteorological elements (e.g. air temperature) and urban texture (e.g. surface albedo) were used to train and test the temporal UHi-model and the Spatial UHi-model respectively. Also, we develop and propose a new metric, UHI-hours, that quantifies the total number of hours that UHI exists in a given area. our results show that UHi-hours is a better indicator of seasonal UHi than the commonly used index, UHi-intensity. consequently, UHi-hours is likely to provide a better measure of the cumulative effects of UHI over time than UHI-intensity. UHI-hours will help us to better quantify the effect of UHI on, for example, the overall daily productivity of outdoor workers or heat-related mortality rates.The world's population is increasing dramatically; new cities are being built, while existing cities are getting overpopulated. Currently, more than 50% of the world population resides in cities 1 . This increased development in urban areas has resulted in changes in urban morphology and surfaces, as well as an increase in the amount of anthropogenic heat released to the atmosphere 2 . The combined effect of such changes leads to higher air temperature in urban areas than in the surrounding rural or suburban areas, hence the formation of heat islands 3 . These heat islands, especially in summers, considerably decrease the outdoor air quality and trigger heat-related diseases and deaths in urban areas 4,5 . Moreover, extreme temperatures have been reported to be the leading cause of weather-related deaths 6 . This is particularly prevalent among the elderly (people of 65 years of age and above) and those with cardiovascular and respiratory health issues. For example, Paravantis et al. 7 analyzed the impact of temperature and heat waves on the number of deaths caused by cardiovascular and respiratory health issues in people above 65 years of age in Athens, Greece. They reported a U-shaped exposure-response curve, indicating reduced mortality rates at moderate temperatures and 20% and 35% increase in mortality rates at very low and very high temperatures, respectively. Furthermore, due to the increase in urban temperatures, there has been an increase in air-conditioning system usage, which in turn has led to high electricity demands and overall increased building energy consumption 8 . At the same time, passive cooling methods, such as natural and night ventilation, have become ineffective for the thermal comfort of building occupants 9 .Due to...

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Section: Methodsmentioning

confidence: 85%

Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea

Ngarambe

Duhirwe

et al. 2020

Sci Rep

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“…where α λ is the surface albedo for the spectral range of λ, ρ TOA i is the TOA reflectance for band i, and c i and c 0 are the regression coefficients [58,66]. The values of c i and c 0 are given in Table 2.…”

Section: Total Shortwave Broadband Albedomentioning

confidence: 99%

Remotely-Sensed Identification of a Transition for the Two Ecosystem States Along the Elevation Gradient: A Case Study of Xinjiang Tianshan Bogda World Heritage Site

et al. 2019

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The alpine treeline, as an ecological transition zone between montane coniferous forests and alpine meadows (two ecosystem states), is a research hotspot of global ecology and climate change. Quantitative identification of its elevation range can efficiently capture the results of the interaction between climate change and vegetation. Digital extraction and extensive analysis in such a critical elevation range crucially depend on the ability of monitoring ecosystem variables and the suitability of the experimental model, which are often restricted by the weak intersection of disciplines and the spatial-temporal continuity of the data. In this study, the existence of two states was confirmed by frequency analysis and the Akaike information criterion (AIC) as well as the Bayesian information criterion (BIC) indices. The elevation range of a transition for the two ecosystem states on the northern slope of the Bogda was identified by the potential analysis. The results showed that the elevation range of co-occurrence for the two ecosystem states was 2690-2744 m. At the elevation of 2714 m, the high land surface temperature (LST) state started to exhibit more attraction than the low LST state. This elevation value was considered as a demarcation where abrupt shifts between the two states occurred with the increase of elevation. The identification results were validated by a field survey and unmanned aerial vehicle data. Progress has been made in the transition identification for the ecosystem states along the elevation gradient in mountainous areas by combining the remotely-sensed index with a potential analysis. This study also provided a reference for obtaining the elevation of the alpine tree line quickly and accurately. Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) noted that recent climate changes have generated widespread impacts on human and natural systems. The atmosphere and oceans are getting warmer, and the amounts of snow and ice are decreasing. Many terrestrial species have shifted their geographic ranges, abundances, and species interactions in response to ongoing climate change [4]. As a "monitor" of climate change, the alpine tree line is sensitive to climate change and can help humans quickly understand the interaction between climate change and vegetation [5][6][7]. In addition, the ecosystem is facing unprecedented pressure, which will restrict regional sustainable development in different regions. Consequently, it is one of the hotspots to study the influence of climate change by obtaining the elevation range of the alpine tree line. At the same time, strengthening the study of the alpine tree line is significant for the protection and sustainable development of Natural Heritage Sites.Many researchers have carried out relevant studies on the alpine tree line related to the formation and restriction mechanisms, location fluctuations under climate change, vegetation community diversity patterns, and demarcation elevation identification. Some studies have taken the limitations o...

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“…In actuality, land surface homogeneity varies with time. We analyzed the land surface homogeniety of the research area with a Landsat 8 OLI data-derived albedo [42], according to the literature [3,43], during dormant and growing seasons. The fitted sill values were both less than 0.001 [44], which indicates that the land surface is indeed homogeneous enough for direct comparison.…”

Section: Land Surface Homogeneitymentioning

confidence: 99%

Design of a Novel Spectral Albedometer for Validating the MODerate Resolution Imaging Spectroradiometer Spectral Albedo Product

2018

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Land surface shortwave broadband albedo is a key parameter in general circulation models and surface energy budget models. Multispectral satellite data are typically used to generate broadband albedo products in a three-step process: atmospheric correction, for converting the top-of-atmosphere observations to surface directional reflectance; angular modeling, for converting the surface directional reflectance to spectral albedo of each individual band; and finally, narrowband-to-broadband conversion, for transforming the spectral albedos to broadband albedos. Spectroradiometers can be used for validating surface directional reflectance products and pyranometers or broadband albedometers, for validating broadband albedo products, but spectral albedo products are rarely validated using ground measurements. In this study, we designed a new type of albedometer that can measure spectral albedos. It consists of multiple interference filters and a silicon detector, for measuring irradiance from 400-1100 nm. The linearity of the sensors is 99%, and the designed albedometer exhibits consistency up to 0.993, with a widely-used commercial instrument. A field experiment for measuring spectral albedo of grassland using this new albedometer was conducted in Yudaokou, China and the measurements are used for validating the MODerate Resolution Imaging Spectroradiometer (MODIS) spectral albedos. The results show that the biases of the MODIS spectral albedos of the first four bands are −0.0094, 0.0065, 0.0159, and −0.0001, respectively. This new instrument provides an effective technique for validating spectral albedos of any satellite sensor in this spectral range, which is critical for improving satellite broadband albedo products.

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Evaluating land surface albedo estimation from Landsat MSS, TM, ETM +, and OLI data based on the unified direct estimation approach

Cited by 99 publications

References 57 publications

Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea

Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea

Remotely-Sensed Identification of a Transition for the Two Ecosystem States Along the Elevation Gradient: A Case Study of Xinjiang Tianshan Bogda World Heritage Site

Design of a Novel Spectral Albedometer for Validating the MODerate Resolution Imaging Spectroradiometer Spectral Albedo Product

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