A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015

Abstract

Surface soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, and critical to meteorology, hydrology, and ecology. The Tibetan Plateau (TP), known as “The third pole of the world” and “Asia’s water towers”, exerts huge influences on and sensitive to global climates. In this situation, longer time series of soil moisture can provide sufficient information to understand the role of the TP. This paper presents the first comprehensive dataset (2002–2015) of spatio-temporal continuous soil moisture at 0.25° resolution, based on satellite-based optical (i.e. MODIS) and microwave (ECV) products using a machine learning method named general regression neural network (GRNN). The dataset itself reveals significant information on the soil moisture and its changes over the TP, and can aid to understand the potential driven mechanisms for climate change over the TP.

Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China

“…The surface soil moisture product, an Essential Climate Variable (ECV) combined product from version V0.44 from European Space Agency (ESA) (https://www.esa-soilmoisture-cci.org/), was generated by merging active microwave, passive microwave, and modelled soil moisture data [39,40].…”

Spatio‐temporal variation of near‐surface soil water content in China from 1988 to 2016

“…A more violent oscillation was found for Qing-Tibet compared with the other six divisions, which may be related to the fact that Qing-Tibet Plateau, known as the Third Pole of the World, was more sensitive to global environmental change because of its unique geographic condition (Peng et al, 2012). Recently, Ma et al (2017) and Cui et al (2019) proposed that as the world's highest plateau (4,500 m above sea level on average), the Tibetan Plateau has experienced more rapid climate warming (0.4°C per decade over the past 50 years) than average, coupled with greater interannual variation in precipitation.…”

“…(2017) and Cui et al. (2019) proposed that as the world's highest plateau (4,500 m above sea level on average), the Tibetan Plateau has experienced more rapid climate warming (0.4°C per decade over the past 50 years) than average, coupled with greater interannual variation in precipitation.…”

A New Fusion Algorithm for Simultaneously Improving Spatio-Temporal Continuity and Quality of Remotely Sensed Soil Moisture Over the Tibetan Plateau

“…There were four input variables in the reference data; three input variables (LST, Albedo, NDVI) needed to be first reconstructed due to cloud contamination. In previous studies, these three variables have been well reconstructed based on the published methods listed in Table 3 [10,15,30]. Reference data were then reprojected and resampled to 0.25°.…”