Global Validation of MODIS Near‐Surface Air and Dew Point Temperatures

Abstract

This analysis is the first global validation of the Moderate Resolution Imaging Spectroradiometer (MODIS)‐derived near‐surface air temperature and dew point estimates, which both serve as crucial input data in models of energy, water, and carbon exchange between terrestrial ecosystems and the atmosphere. By hypsometrically interpolating the MOD07 Level‐2 atmospheric profile product to surface pressure level, we obtained near‐surface air temperature and dew point observations at 5 km pixel resolution. We compared these daily data, retrieved over a 14‐year record, to corresponding measurements from 109 ground meteorological stations (FLUXNET). Our results show strong agreement between satellite and in situ near‐surface air temperature measurements (R2 = 0.89, root‐mean‐square error = 3.47°C, and bias = −0.19°C) and dew point observations (R2 = 0.76, root‐mean‐square error = 5.04°C, and bias = 0.79°C) with insignificant differences in error across climate zones. This validation is among the earliest assessments of the reprocessed, crosstalk‐corrected Collection 6.1 Terra MODIS data and provides support for widespread applications of near‐surface atmospheric data.

ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station

“…Landsat is used for ancillary surface properties NDVI, SAVI, and albedo. MODIS is used for ancillary atmospheric properties (with cloud gap-filling from the National Centers for Atmospheric Prediction) and to gap-fill cloudy Landsat surface properties (MODIS surface products are provided as gap-filled multiday aggregates) (Famiglietti et al, 2018;Fisher & ECOSTRESS algorithm development team, 2015;Verma et al, 2016). PT-JPL, PM-Mu, and SEBS/TSEB are all forced with the same input data for shared variables, including uniform calculation of, for example, R n and G. The multimodel uncertainty product provides spatiotemporally varying information, necessary because a constant uncertainty value cannot be applied to the ET estimates.…”

New ECOSTRESS and MODIS Land Surface Temperature Data Reveal Fine-Scale Heat Vulnerability in Cities: A Case Study for Los Angeles County, California

“…However at night, the LST and Tair will display similar spatial and temporal patterns due to reduced advection, solar shading, and greater atmospheric boundary layer stability [39,40,45]. Numerous studies have derived regression functions for estimating Tair from the LST [40,[46][47][48][49] or Tair from air temperature profiles [50] on continental to global scales at coarse kilometer-scale resolution, but far fewer have developed robust relationships in urban environments due to the increased complexity of surface-air relationships in the built up urban environment [51][52][53]. Complicating the problem further are the sparsity of station air temperature measurements and the kilometer-scale spatial resolution of typical model derived urban air temperatures, e.g., the Weathe Research and Forecasting (WRF)-urban canopy model [54], that do not correspond to the LST data at finer scales.…”

Estimating Daily Dew Point Temperature Using Machine Learning Algorithms

“…Following that, a linear relationship is obtained between the input and output data in the converted space. The general form of the linear relation is as follows: (1) where, f(x) is the estimated variable, W T is the transpose of the vector of coefficients and b is a constant coefficient. The SVM is based on minimizing the value of empirical error.…”

“…Dew point temperature (DPT) is defined as the temperature in which air becomes liquid water due to the high concentration of water molecules. Precise and accurate estimation of DPT has a significant role in solving agricultural problems, such as calculating the amount of available moisture in the air and estimating the near surface humidity [1]. DPT and relative humidity are commonly used to measure the air humidity level [2].…”