Modeling the Distributions of Brightness Temperatures of a Cropland Study Area Using a Model that Combines Fast Radiosity and Energy Budget Methods

Zunjian Bian,Qinhuo Liu,Biao Cao,Huaguo Huang,Hua Li,Yongming Du,Qing Xiao

doi:10.3390/rs10050736

Abstract

Land surface temperatures (LSTs) obtained from remote sensing data are crucial in monitoring the conditions of crops and urban heat islands. However, since retrieved LSTs represent only the average temperature states of pixels, the distributions of temperatures within individual pixels remain unknown. Such data cannot satisfy the requirements of applications such as precision agriculture. Therefore, in this paper, we propose a model that combines a fast radiosity model, the Radiosity Applicable to Porous IndiviDual Objects (RAPID) model, and energy budget methods to dynamically simulate brightness temperatures (BTs) over complex surfaces. This model represents a model-based tool that can be used to estimate temperature distributions using fine-scale visible as well as near-infrared (VNIR) data and temporal variations in meteorological conditions. The proposed model is tested over a study area in an artificial oasis in Northwestern China. The simulated BTs agree well with those measured with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The results reflect root mean squared errors (RMSEs) less than 1.6 °C and coefficients of determination (R2) greater than 0.7. In addition, compared to the leaf area index (LAI), this model displays high sensitivity to wind speed during validation. Although simplifications may be adopted for use in specific simulations, this proposed model can be used to support in situ measurements and to provide reference data over heterogeneous vegetation surfaces.

Highlights

Land surface temperature (LST) is always treated as a vital variable in the physical processes of surface-atmosphere interactions such as the energy budget and the hydrological cycle [1,2]
The surface brightness temperatures (BTs) were simulated with a spatial resolution of 90.0 m in order to match the ASTER pixels
The low root mean squared errors (RMSEs) values obtained for the two selected days (1.33 ◦C for 0710 and 1.55 ◦C for 0802) indicate that the Radiosity Applicable to Porous IndiviDual Objects (RAPID)-EB model displays acceptable performance when simulating surface BTs

Summary

Introduction

Land surface temperature (LST) is always treated as a vital variable in the physical processes of surface-atmosphere interactions such as the energy budget and the hydrological cycle [1,2]. For global-scale applications, the spatial resolution of thermal infrared (TIR) pixels collected by many geostationary or polar-orbit satellites is as coarse as 1.0 km or 5.0 km. In such cases, heterogeneous pixels are very common. Over mixed pixels that cover vegetation and urban areas, the temperature differences between components can reach up to 20 ◦C [14] This level of discrepancy is not conducive to quantitative applications such as precision agriculture and sustainable urban design, which typically require finely resolved LSTs. In addition, the lack of a surface temperature distribution limits our understanding of the accuracy of coarse-scale LST products over heterogeneous surfaces due to scale problems

Methods

Results

Discussion

Conclusion