Cloud tolerance of remote-sensing technologies to measure land surface temperature

Abstract

Abstract. Conventional methods to estimate land surface temperature (LST) from space rely on the thermal infrared (TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passivemicrowave (MW) observations. The MW-LST product is informed by six polar-orbiting satellites to create a global record with up to eight observations per day for each 0.25 • resolution grid box. For days with sufficient observations, a continuous diurnal temperature cycle (DTC) was fitted. The main characteristics of the DTC were scaled to match those of a geostationary TIR-LST product.This paper tests the cloud tolerance of the MW-LST product. In particular, we demonstrate its stable performance with respect to flux tower observation sites (four in Europe and nine in the United States), over a range of cloudiness conditions up to heavily overcast skies. The results show that TIRbased LST has slightly better performance than MW-LST for clear-sky observations but suffers an increasing negative bias as cloud cover increases. This negative bias is caused by incomplete masking of cloud-covered areas within the TIR scene that affects many applications of TIR-LST. In contrast, for MW-LST we find no direct impact of clouds on its accuracy and bias. MW-LST can therefore be used to improve TIR cloud screening. Moreover, the ability to provide LST estimates for cloud-covered surfaces can help expand current clear-sky-only satellite retrieval products to all-weather applications.

Towards Estimating Land Evaporation at Field Scales Using GLEAM

“…Nowadays, the use of LST-derived from thermal infrared (TIR) sensors onboard satellites [25], cubesats [26], or drones [27]-in these algorithms allows for estimating land evaporation at high spatial (up to 1 m using drones) and temporal resolutions (up to several minutes using geostationary imagery), with a limited need for ancillary data [25]. However, TIR measurements are affected by clouds [28], restricting the estimation of land evaporation to times with optimal weather conditions [29]. While estimates of LST from microwave (MW) observations might overcome this problem [28,30], they suffer from a substantially coarser native spatial resolution (several kilometres).…”

“…However, TIR measurements are affected by clouds [28], restricting the estimation of land evaporation to times with optimal weather conditions [29]. While estimates of LST from microwave (MW) observations might overcome this problem [28,30], they suffer from a substantially coarser native spatial resolution (several kilometres). In addition to these issues, energy-balance methods often target the accurate estimation of the sensible heat flux, relying on assumptions regarding the vertical temperature gradient and the aerodynamic conductance of the atmosphere, and calculate the evaporative flux as the residual of the surface energy balance [22][23][24]31].…”

Microwave implementation of two-source energy balance approach for estimating evapotranspiration

“…Together they can be used to construct a diurnal cycle of brightness temperature for each location on Earth (Holmes et al, 2013b;Norouzi et al, 2012). This diurnal brightness temperature can then be scaled to match the diurnal temperature cycle as measured by TIR imagers (Holmes et al, 2015(Holmes et al, , 2016.…”

“…One MW frequency band with a particularly high sensitivity to LST (Prigent et al, 2016) and high tolerance to clouds (Holmes et al, 2016) is . MW radiometers with a Ka-band channel are available from several low Earth orbiting satellites 30 that sample at different times of the day.…”

“…This is based on day-night temperature differences from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite from NASA's Earth Observing System (EOS) program (Hain and Anderson, in review). Reliance on TIR effectively limits surface observations to clear skies (Rossow et al, 1989), and failure to completely mask cloud affected observations is shown to limit the precision in TIR-LST (Holmes et al, 2016). Continuous daily estimates of ET are generated from clear-sky ALEXI samples through temporal 20 interpolation based on maintaining a normalized flux partitioning metric.…”