Abstract
Land surface temperature (LST) is a key variable in the study of the energy exchange between the land surface and the atmosphere. Among the different methods proposed to estimate LST, the quadratic split-window (SW) method has achieved considerable popularity. This method works well when the emissivities are high in both channels. Unfortunately, it performs poorly for low land surface emissivities (LSEs). To solve this problem, assuming that the LSE is known, the constant in the quadratic SW method was calculated by maintaining the other coefficients the same as those obtained for the black body condition. This procedure permits transfer of the emissivity effect to the constant. The result demonstrated that the constant was influenced by both atmospheric water vapour content (W) and atmospheric temperature (T0) in the bottom layer. To parameterize the constant, an exponential approximation between W and T0 was used. A LST retrieval algorithm was proposed. The error for the proposed algorithm was RMSE = 0.70 K. Sensitivity analysis results showed that under the consideration of NEΔT = 0.2 K, 20% uncertainty in W and 1% uncertainties in the channel mean emissivity and the channel emissivity difference, the RMSE was 1.29 K. Compared with AST 08 product, the proposed algorithm underestimated LST by about 0.8 K for both study areas when ASTER L1B data was used as a proxy of Gaofen-5 (GF-5) satellite data. The GF-5 satellite is scheduled to be launched in 2017.
Highlights
As a key parameter of the surface energy budget, land surface temperature (LST) is directly related to surface energy fluxes and to the latent heat flux and water stress in particular [1,2]
To evaluate the performance of Equations (11) and (12), the algorithm with W developed by Sobrino and Raissouni based on the quadratic SW method was used for comparison
The results indicate that a noise equivalent differential temperature (NE∆T) of more than 0.2 K in the infrared channels of the multiple spectral-imager (MSI) onboard the GF-5 satellite can yield the relative large error for LST retrieval
Summary
As a key parameter of the surface energy budget, land surface temperature (LST) is directly related to surface energy fluxes and to the latent heat flux (evapotranspiration) and water stress in particular [1,2]. The LST is crucial for estimating the net radiation driven by the surface longwave emission [3] and for computing soil moisture [4,5]. LST is an essential climate variable for understanding meteorological and hydrological processes in a changing climate [6,7,8]. Understanding and monitoring the dynamics of the LST is critical for modelling and predicting climate and environmental changes and for other applications such as agriculture, urban heat island and vegetation monitoring [9,10,11,12]. One of its missions is to collect land information at high spatial resolution from visible to thermal infrared (TIR) spectral
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