Comparative Assessment of Two Vegetation Fractional Cover Estimating Methods and Their Impacts on Modeling Urban Latent Heat Flux Using Landsat Imagery

Abstract

Quantifying vegetation fractional cover (VFC) and assessing its role in heat fluxes modeling using medium resolution remotely sensed data has received less attention than it deserves in heterogeneous urban regions. This study examined two approaches (Normalized Difference Vegetation Index (NDVI)-derived and Multiple Endmember Spectral Mixture Analysis (MESMA)-derived methods) that are commonly used to map VFC based on Landsat imagery, in modeling surface heat fluxes in urban landscape. For this purpose, two different heat flux models, Two-source energy balance (TSEB) model and Pixel Component Arranging and Comparing Algorithm (PCACA) model, were adopted for model evaluation and analysis. A comparative analysis of the NDVI-derived and MESMA-derived VFCs showed that the latter achieved more accurate estimates in complex urban regions. When the two sources of VFCs were used as inputs to both TSEB and PCACA models, MESMA-derived urban VFC produced more accurate urban heat fluxes (Bowen ratio and latent heat flux) relative to NDVI-derived urban VFC. Moreover, our study demonstrated that Landsat imagery-retrieved VFC exhibited greater uncertainty in obtaining urban heat fluxes for the TSEB model than for the PCACA model.

Identification of Potential High-Risk Habitats within the Transmission Reach of Oncomelania hupensis after Floods Based on SAR Techniques in a Plane Region in China

“…Most Vegetation indices, including the leaf area index (LAI), fractional vegetation cover, and biomass [ 34 , 35 , 36 , 37 ], derived from satellite images are based on algebraic combinations of reflectance in the red, R, and near-infrared, NIR, spectral bands [ 38 , 39 , 40 , 41 ]. After atmospheric correction through Spectral Hypercubes (FLAASH) module, the normalized difference vegetation index (NDVI), fractional vegetation cover (Fv) and land surface temperature (LST) were extracted from Landsat 8 image using ENVI 5.3 software (Exelis Inc., Boulder, CO, USA) via algebraic band combinations [ 42 , 43 , 44 , 45 ].…”

Surface Temperature and Heat Fluxes: Comparison between Natural Forest and Oil Palm Plantation in Jambi Province Using Surface Energy Balance Algorithm for Land (SEBAL)

“…Perubahan nilai albedo dapat mempengaruhi suhu permukaan, dan neraca energi permukaan pada skala lokal (Burakowski et al, 2018;Trlica et al, 2017). Lahan terbuka dengan vegetasi rendah memiliki suhu permukaan dan fluks bahang terasa lebih tinggi, namun memiliki fluks bahang laten yang lebih rendah dibandingkan dengan tanaman tinggi berkayu (Liu et al, 2017). Metode Surface Energy Balance Algorithm for Land (SEBAL) dapat mengestimasi nilai suhu permukaan dan komponen neraca energi, yaitu nilai fluks bahang tanah (G), fluks bahang terasa (H), dan fluks bahang laten (LE) dengan akurasi cukup baik di berbagai tutupan lahan (Bhattarai et al, 2016;Jassas et al, 2015).…”

Impervious Surface Area Patterns and Their Response to Land Surface Temperature Mechanism in Urban–Rural Regions of Qingdao, China

“…Subsequently, to obtain a more accurate LST spatial pattern, the single window model was upgraded to the split algorithm model method in response to the emergence of Landsat 8/9 Operational Land Imager (OLI) satellite imagery, which had two thermal infrared bands [33,34]. Furthermore, the pixel component arranging and component algorithm (PCACA) model can investigate the mechanisms of temperature and energy changes on the land surface [35]. Through the PCACA model, the spatial coefficients of sensible heat flux and latent heat flux were obtained; these were input into the surface radiation energy balance model to generate mechanism indicators such as latent heat flux, sensible heat flux, soil heat flux, and net radiation [35].…”