Estimation of Land Surface Temperature under Cloudy Skies Using Combined Diurnal Solar Radiation and Surface Temperature Evolution

Abstract

Land surface temperature (LST) is a key parameter in the interaction of the land-atmosphere system. However, clouds affect the retrieval of LST data from thermal-infrared remote sensing data. Thus, it is important to determine a method for estimating LSTs at times when the sky is overcast. Based on a one-dimensional heat transfer equation and on the evolution of daily temperatures and net shortwave solar radiation (NSSR), a new method for estimating LSTs under cloudy skies (Tcloud) from diurnal NSSR and surface temperatures is proposed. Validation is performed against in situ measurements that were obtained at the ChangWu ecosystem experimental station in China. The results show that the root-mean-square error (RMSE) between the actual and estimated LSTs is as large as 1.23 K for cloudy data. A sensitivity analysis to the errors in the estimated LST under clear skies (Tclear) and in the estimated NSSR reveals that the RMSE of the obtained Tcloud is less than 1.5 K after adding a 0.5 K bias to the actual Tclear and 10 percent NSSR errors to the actual NSSR. Tcloud is estimated by the proposed method using Tclear and NSSR products of MSG-SEVIRI for southern Europe. The results indicate that the new algorithm is practical for retrieving the LST under cloudy sky conditions, although some uncertainty exists. Notably, the approach can only be used during the daytime due to the assumption of the variation in LST caused by variations in insolation. Further, if there are less than six Tclear observations on any given day, the method cannot be used.

Temporal interpolation of land surface fluxes derived from remote sensing – results with an unmanned aerial system

“…Traditionally, the applicability of land surface models is limited due to complex model parameterization and the limited availability of "ground truth" or in situ data for parameter calibration. As shown in this study, one solution for this limitation is using remote-sensing-based observations or estimates as "ground truth" for model calibration (Stisen et al, 2011;Zhang et al, 2009). This study calibrated the model parameters through remote sensing snapshot (UAS) estimates of land surface variables such as T s and θ and provided an example of integrating remote sensing data and process-based models.…”

A Four‐Parameter Model for Estimating Diurnal Temperature Cycle From MODIS Land Surface Temperature Product

“…Once the parameters are determined, surface temperature at any time of a day is able to be predicted. Besides, DTC models are helpful for atmospheric corrections on satellite observations (Jiang et al., 2006; Schädlich et al., 2001), temporal normalization of remotely sensed LSTs (Jiang, 2007), estimation of LST under cloudy condition (Liu et al., 2017; Zhang et al., 2015), estimation of surface air temperature (Gholamnia et al., 2017), and improvement of cloud‐screening algorithms (Inamdar et al., 2008). They are also promising to be used in simultaneously retrieving LST and Land Surface Emissivity (LSE) from multi‐spectral and multi‐temporal observations of geostationary satellites (Li, Tang et al., 2013).…”

Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information

“…Lu, et al [19] improved the neighboring pixel method by introducing satellite observations. Zhang, et al [20] used a one-dimensional heat transfer model to reconstruct cloudy pixels. In statistical methods, the solution for recovering cloudy MODIS LST pixels is to introduce microwave temperature brightness (TB) data due to penetrating clouds.…”