Estimating High Spatial Resolution Air Temperature for Regions with Limited in situ Data Using MODIS Products

Abstract

Abstract:The use of land surface temperature and vertical temperature profile data from Moderate Resolution Imaging Spectroradiometer (MODIS), to estimate high spatial resolution daily and monthly maximum and minimum 2 m above ground level (AGL) air temperatures for regions with limited in situ data was investigated. A diurnal air temperature change model was proposed to consider the differences between the MODIS overpass times and the times of daily maximum and minimum temperatures, resulting in the improvements of the estimation in terms of error values, especially for minimum air temperature. Both land surface temperature and vertical temperature profile data produced relatively high coefficient of determination values and small Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for air temperature estimation. The correction of the estimates using two gridded datasets, National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and Climate Research Unit (CRU), was performed and the errors were reduced, especially for maximum air temperature. The correction of daily and monthly air temperature estimates using the NCEP/NCAR reanalysis data, however, still produced relatively large error values compared to existing studies, while the correction of monthly air temperature estimates using the CRU data significantly reduced the errors; the MAE values for estimating monthly maximum air temperature range between 1.73 °C and 1.86 °C. Uncorrected land surface temperature generally performed better for estimating monthly minimum air temperature and the MAE values range from 1.18 °C to 1.89 °C. The suggested OPEN ACCESSRemote Sens. 2014, 6 7361 methodology on a monthly time scale may be applied in many data sparse areas to be used for regional environmental and agricultural studies that require high spatial resolution air temperature data.

Landsat Time-series Images-based Urban Heat Island Analysis: The Effects of Changes in Vegetation and Built-up Land on Land Surface Temperature in Summer in the Hanoi Metropolitan Area, Vietnam

“…Data from Table 2 illustrates the minimum and maximum differences in values between estimated LSTs and the meteorological air temperatures (MATs) were 1.1-4.2 o C, 0.9-4.0 o C and 0.6-4.0 o C at the three time points, respectively. These positive differences are similar to those reported in previous studies of Chan et al (2018) and Rhee and Im (2014) that the day LST is higher than air temperature. The two main metrics, root mean square error (RMSE) and mean absolute error (MAE), were determined with the values of 2.3 o C and 2.0 o C on 30 September 1996, 2.6 o C and 2.6 o C on 24 May 2007, and 2.2 o C and 1.9 o C on 01 June 2016.…”

Application of MODIS land surface temperature data: a systematic literature review and analysis

“…The total number is not equal to 52 because two studies were applied for both daily and weekly Ta, 48,123 and one study was applied to estimate both daily and monthly Ta. 124 In addition, the difference between daily, weekly, and monthly articles also suggests that the high temporal resolution of MODIS LST data (four times daily) is one of the largest advantages of MODIS data and is, therefore, widely applied for Ta estimation.…”

Relationship between land cover patterns and surface temperature in urban areas

“…Mohan et al (2013) assessed the urban heat island intensity in the megacity of Delhi and identified many hot spots of temperature in densely populated areas and commercial regions. Thus, there has been an effort to estimate air temperature using remote sensing to better understand heat island effect (Miliaresis and Tsatsaris 2011;Hill 2013;Rhee and Im 2014).…”