A simple empirical method for estimating surface water vapour pressure using MODIS near-infrared channels: applications to northern Spain's Asturias region

Abstract

In this article, we present a simple methodology for obtaining algorithms to estimate surface water vapour pressure (e 0 ) over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The algorithm obtained in this case is adapted to the particular climatic characteristics of the Asturias region, but the methodology can easily be extrapolated and used to obtain algorithms for other regions around the world. The proposed method estimates e 0 from a simple linear combination of the radiances of the MODIS near-infrared (NIR) channels more commonly applied to total precipitable water (W ) estimations. Comparison between the e 0 data measured at the ground-based meteorological stations in Asturias (daily data from 2004) versus the values predicted using the proposed algorithm gives R 2 = 0.76 and residual standard error (RSE) = 2.07 hPa (16%). The algorithm was tested using the data from 2008 obtained in Asturias and in two sites outside of Asturias with similar latitudes and radiosonde observations (La Coruña and Santander). The resulting validation demonstrates that the algorithm gives good results in Asturias (root-mean-square deviation (RMSD) = 2.50 hPa (19%) and bias = 1.26 hPa, with R 2 = 0.65) and when La Coruña is included (R 2 = 0.61), but that its validity is decreased when Santander is also included (R 2 = 0.56).The possibility of obtaining e 0 from three global MODIS algorithms for W retrieval was also tested and compared to our algorithm. The results show that our algorithm gives better results than the International MODIS/Atmospheric InfraRed Sounder Processing Package (IMAPP) Water Vapour Near-Infrared (WVNIR) product and the Sobrino algorithm. The MODIS Total Precipitable Water (MOD05) product is worse than that obtained with our algorithm in Asturias (R 2 = 0.61 vs. R 2 = 0.65), but the two values are similar if the stations in La Coruña (R 2 = 0.60) and Santander (R 2 = 0.56) are included in the comparison. The dominant advantage of the novel algorithm proposed in this study is that it is simpler and can be produced quickly in real time.

A Remote Sensing Method for Estimating Surface Air Temperature and Surface Vapor Pressure on a Regional Scale

“…Hashimoto et al [22] estimated the spatial distribution of VPD with a simple linear relationship between it and LST retrieved from MODIS. With the availability of satellite total precipitable water (TPW) products, some studies have used TPW to estimate ea by establishing the regression functions between TPW and ea or air-specific humidity, such as the approaches presented by Sobrino et al [23] and Recondo et al [24]. However, the relationship between TPW and ea is empirical, which can only apply to the specific area by the limitation of the training dataset.…”

An improved satellite‐based approach for estimating vapor pressure deficit from MODIS data

“…Recondo et al . [] proposed a simple algorithm to estimate the surface water vapor pressure directly from MODIS NIR channels; validation of the algorithm in two test stations using data collected in 2008 demonstrated good results in their research region, with mean absolute error (MAE) and RMSE values of 0.15 kPa and 0.25 kPa, respectively, and coefficients of determination ( R 2 ) ranging from 0.56 to 0.65. Recondo et al .…”

“…Although Recondo et al . [, ] have achieved comparatively high accuracy, their method still relies only on the W‐e a relation.…”

“…Prince et al [1998] inferred near-surface t dew and VPD from the precipitable water content retrieved by NOAA/AVHRR satellite data. Recondo et al [2013a] proposed a simple algorithm to estimate the surface water vapor pressure directly from MODIS NIR channels; validation of the algorithm in two test stations using data collected in 2008 demonstrated good results in their research region, with mean absolute error (MAE) and RMSE values of 0.15 kPa and 0.25 kPa, respectively, and coefficients of determination (R 2 ) ranging from 0.56 to 0.65. Recondo et al [2013b] presented similar empirical models for estimating daily surface e a from MODIS data, with model validations yielding R 2 = 0.83, 0.79, and 0.7 and RMSE = 0.19, 0.2, and 0.22 kPa for e a_mean , e a_max , e a_min , respectively.…”

Third International Symposium on Recent Advances in Quantitative Remote Sensing

“…, soil moisture and evapotranspiration(Corbari et al 2013; Dente et al 2013;Recondo et al 2013;Sepulcre-Cantó et al 2013;Song et al 2013;Tang et al 2013;Zhao and Li 2013), and various other land applications(Abbott, Gillespie, and Kahle 2013; Amici et al 2013; Arenas-Castro et al 2013; Calle, Salvador, and González-Alonso 2013; Cierniewski et al 2013; Danilina et al 2013;Lagouarde et al 2013;Le Hegarat-Mascle and Ottle 2013; …”