Estimation of Daily Air Temperature Based on MODIS Land Surface Temperature Products over the Corn Belt in the US

Abstract

Air temperature (Ta) is a key input in a wide range of agroclimatic applications. Moderate Resolution Imaging Spectroradiometer (MODIS) Ts (Land Surface Temperature (LST)) products are widely used to estimate daily Ta. However, only daytime LST (Ts-day) or nighttime LST (Ts-night) data have been used to estimate Tmax/Tmin (daily maximum or minimum air temperature), respectively. The relationship between Tmax and Ts-night, and the one between Tmin and Ts-day has not been studied. In this study, both the ability of Ts-night data to estimate Tmax and the ability of Ts-day data to estimate Tmin were tested and studied in the Corn Belt during the growing season (May-September) from 2008 to 2012, using MODIS daily LST products from both Terra and Aqua. The results show that using Ts-night for estimating Tmax could result in a higher accuracy than using Ts-day OPEN ACCESSRemote Sens. 2015, 7 952 for a similar estimate. Combining Ts-day and Ts-night, the estimation of Tmax was improved by 0.19-1.85, 0.37-1.12 and 0.26-0.93 °C for crops, deciduous forest and developed areas, respectively, when compared with using only Ts-day or Ts-night data. The main factors influencing the Ta estimation errors spatially and temporally were analyzed and discussed, such as satellite overpassing time, air masses, irrigation, etc.

An Online System for Nowcasting Satellite Derived Temperatures for Urban Areas

“…The TVX approach exploits the negative relationship between NDVI and LST [36,37] but has limited utility in urban areas and also in high-latitude regions and elevations. This is because the NDVI-LST relationship is more related to the available solar radiation (which is low for high latitude environments) than the land surface conditions [31]. The last category is the surface energy balance parameterizations which need many ancillary datasets (e.g., soil physical properties) and consider the sum of the incoming net radiation and the anthropogenic heat fluxes to be equal to the sum of the soil heat, the sensible, and the latent heat fluxes [29,31].…”

“…This is because the NDVI-LST relationship is more related to the available solar radiation (which is low for high latitude environments) than the land surface conditions [31]. The last category is the surface energy balance parameterizations which need many ancillary datasets (e.g., soil physical properties) and consider the sum of the incoming net radiation and the anthropogenic heat fluxes to be equal to the sum of the soil heat, the sensible, and the latent heat fluxes [29,31]. However, most of the aforementioned methods either are confined to certain locations and time spots or have not been tested extensively [19,40].…”

“…In total, three major categories of relevant algorithms can be identified: the statistical approaches that correlate LST with TA [18,20,[24][25][26][27][28][29][30][31][32][33][34][35]; the temperature-vegetation index (TVX) approaches [23,36,37]; and the surface energy-balance parameterizations [38,39]. The first category can be divided further into the simple statistical approaches, which are usually based on a linear regression between the LST and TA, and the advanced statistical approaches.…”

“…The first category can be divided further into the simple statistical approaches, which are usually based on a linear regression between the LST and TA, and the advanced statistical approaches. The simple approaches cannot be generally applied and perform well only at the locations for which they were developed [29][30][31]. The advanced approaches utilize two or more parameters, such as the LST, the altitude and the Normalized Vegetation Difference Index (NDVI), and have been tested for different combinations of satellite data.…”

“…The advanced approaches utilize two or more parameters, such as the LST, the altitude and the Normalized Vegetation Difference Index (NDVI), and have been tested for different combinations of satellite data. For instance, Cresswell [27] used the solar zenith angle (SZA) as a proxy to TA, Jang et al [28] the altitude, SZA, and information from the five channels of the Advanced Very High Resolution Radiometer (AVHRR), and Zeng et al [31] the altitude, SZA, irrigation data, and cropland-type data. It has also been observed that the LST-TA correlations used in statistical approaches are better during nighttime because the microscale advection is reduced and more complex during daytime where additional environmental factors, e.g., solar illumination, skyview factor, etc., are at play [30].…”