Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China

Yuan Yang,Jian Yang,Wen Cai

doi:10.3390/rs9050410

Abstract

Air temperature (Tair) near the ground surface is a fundamental descriptor of terrestrial environment conditions and one of the most widely used climatic variables in global change studies. The main objective of this study was to explore the possibility of retrieving high-resolution Tair from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products, covering complex terrain in Northeast China. The All Subsets Regression (ASR) method was adopted to select the predictors and build optimal multiple linear regression models for estimating maximum (Tmax), minimum (Tmin), and mean (Tmean) air temperatures. The relative importance of predictors in these models was evaluated via the Standardized Regression Coefficients (SRCs) method. The results indicated that the optimal models could estimate the Tmax, Tmin, and Tmean with relatively high accuracies (Model Efficiency ≥ 0.90). Both LST and day length (DL) predictors were important in estimating Tmax (SRCs: daytime LST = 0.53, DL = 0.35), Tmin (SRCs: nighttime LST = 0.74, DL = 0.23), and Tmean (SRCs: nighttime LST = 0.72, DL = 0.28). Models predicting Tmin and Tmean had better performance than the one predicting Tmax. Nighttime LST was better at predicting Tmin and Tmean than daytime LST data at predicting Tmax. Land covers had noticeable influences on estimating Tair, and even seasonal vegetation greening could result in temporal variations of model performance. Air temperature could be accurately estimated using remote sensing, but the model performance was varied across different spatial and temporal scales. More predictors should be incorporated for the purpose of improving the estimation of near surface Tair from the MODIS LST production.

Highlights

Air temperature (Tair ) near the ground surface is a fundamental descriptor of terrestrial environment conditions [1,2] and one of the most widely used climatic variables in global change studies
The specific objectives of this study were to (1) evaluate relative importance of the driving factors influencing the relationship between land surface temperature (LST) and eight-day Tair, (2) develop optimal models to predict Tair covering a large area that encompasses a broad variation of physiognomy and physiognomy land cover, and (3) models’
The variation of model variation of model performance mainly depended on the spatial heterogeneity (i.e., topography, performance mainly depended on the spatial heterogeneity and time land cover) and time cycle

Summary

Introduction

Air temperature (Tair ) near the ground surface is a fundamental descriptor of terrestrial environment conditions [1,2] and one of the most widely used climatic variables in global change studies. It plays an important role in multiple biological and physical processes among the hydrosphere, atmosphere, and biosphere [3,4,5]. Accurate estimation of Tair and mapping its spatial distribution are useful for predicting ecological consequences of climate change. The demand for accurate spatial Tair data over large scale has continued to rise [12,13]. Air temperature is often measured in thermometer shelters 1.5 m–2 m above the ground at meteorological stations, and is a commonly recorded form of meteorological observation data with

Objectives

Methods

Discussion

Conclusion