Retrieval of the land surface‐air temperature difference from high spatial resolution satellite observations over complex surfaces in the Tibetan Plateau

Abstract

The temperature difference between the surface and the air (dTsa) directly indicates the intensity and heat fluxes of land-atmosphere interaction. Considering the effects of surface characteristics and air condition on the surface temperature, using 1 km data from the MOD02 thermal infrared bands of the EOS/moderate-resolution imaging spectroradiometer (MODIS) on satellite Aqua, other MODIS products and temperatures observed from weather stations at 14:00 China standard time (CST), the study analyzes the relationships between dTsa and brightness temperature in the infrared atmospheric window band (Bt31 and Bt32), the water vapor band (Bt28), the atmospheric temperature band (Bt25), and the CO 2 band (Bt34). A model estimating dTsa is built. The model coefficients are estimated for 96 stations representing 96 sets of surface and atmospheric conditions, and 71 sets of coefficients among them passing 90% confidence levels of estimating dTsa are selected as references. Combined with the probabilistic neural network (PNN) method and nine parameters reflecting surface characteristics in one season and month, the Tibetan Plateau surface is classified as 71 types with 71 sets of coefficients. PNN is certified an efficient classification method for multiple parameters and mass data. Based on PNN and estimated model, estimated dTsa shows 1.36°C root-mean-square error and a standard deviation of 0.74°C, and dTsa distribution exhibits all centers with peak value and valley value of European Centre for Medium-Range Weather Forecasts, MYD07, and simple regression model results, showing its superiority. The model is worthy of further exploration and application in an effort to advance the retrieval of surface energy fluxes from remote sensing.

Data‐Guided Exploration of the Energy Partitioning at Land Surface in the Contiguous US

“…These two mediating factors are not arbitrarily selected, but based on hydrological knowledge (Rodriguez-Iturbe et al, 1991; J. Zhang et al, 2015;Q. Zhang et al, 2011).…”

“…The land-air temperature difference dT is an important factor that is directly related to land-air energy flux interaction intensity (J. Zhang et al, 2015;Q. Zhang et al, 2011).…”

Extracting Taklimakan Dust Parameters from AIRS with Artificial Neural Network Method

“…This is mainly caused by the response difference at visible and infrared waveband. It should be noted that ideally the training should be done separately for daytime and nighttime since the brightness temperature (BT) differences between daytime and nighttime are large due to the surface skin temperature difference over desert [45]. Using multiple IR window channels might reduce the effects of skin temperature differences, and nighttime training is still needed when there are enough samples collected.…”

Introduction to the Special Section on Fast Physics in Climate Models: Parameterization, Evaluation, and Observation

“…Zhang, J. et al () examine the difference between the surface and the air temperature (dTsa) and its link to heat fluxes over the Tibetan Plateau, using 1‐km data from the thermal infrared bands of the Moderate‐Resolution Imaging Spectroradiometer (MODIS) on satellite Aqua, other MODIS products, and the temperatures observed from weather stations. An approach for estimating dTsa is developed that combines the probabilistic neural network classification with the multivariate regression.…”