A Method for Deriving Relative Humidity From MODIS Data Under All-Sky Conditions

Liao, Qian‐Yu; Leng, Pei; Li, Zhao-Liang; Ren, Chao; Sun, Yayong; Gao, Maofang; Duan, Si‐Bo; Shang, Gao

doi:10.1109/tgrs.2020.3036248

Cited by 7 publications

(1 citation statement)

References 57 publications

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“…However, the MODIS LST has a limitation that is not available under cloudy sky conditions. Liao et al [29] proposed a method to estimate RHns under clouds using Ta and LST, which were obtained under clear skies [29]. They also used a linear regression model to estimate the actual water vapor based on the precipitable water vapor product derived from MODIS.…”

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confidence: 99%

Estimation of Spatially Continuous Near-Surface Relative Humidity Over Japan and South Korea

Park

Lee

Yoo

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Near-surface relative humidity (RHns) is an essential meteorological parameter for water, carbon, and climate studies. However, spatially continuous RHns estimation is difficult due to the spatial discontinuity of in-situ observations and the cloud contamination of satellite-based data. This study proposed machine learning-based models to estimate spatially continuous daily RHns at 1 km resolution over Japan and South Korea under all sky conditions and examined the spatiotemporal patterns of RHns. All sky estimation of RHns using machine learning has been rarely conducted, and it can be an alternative to the currently available RHns data mostly from numerical models, which have relatively low spatial resolution. We combined two schemes for clear sky conditions (scheme A, which uses satellite and reanalysis data) and cloudy sky conditions (scheme B, which uses reanalysis data solely). The relatively small numbers of data in extremely low and high RHns conditions (i.e., < 30% or > 70%, respectively) were augmented by applying an oversampling method to avoid biased training. The machine learning models based on random forest and XGBoost were trained and validated using 94 in-situ observation sites from meteorological administrations of both countries from 2012 to 2017. The results showed that XGBoost produced slightly better performance than random forest, and the spatially continuous RHns model combined based on XGBoost yielded the coefficient of determination of 0.72 and a root mean square error of 10.61%. Spatiotemporal patterns of the estimated RHns agreed with in-situ observations, reflecting the effect of topography on RHns. We expect that the proposed RHns model could be used in various environmental studies that require RHns under all sky conditions as input data.

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