Abstract
Abstract Soil moisture represents many attributes of the geo-hydrological cycle and the climate system. Citizen science through social media as an emerging tool could be utilized to collect soil moisture data. A pilot study area was selected in Shahriar, Iran. A user interface and a sampling process (use of citizen science by subscribers) were designed to analyze the subjective and gravimetric soil moisture data. Furthermore, explanatory moisture condition (EMC), a new initiative to consider land use in soil moisture information from vegetation cover, was evaluated. A statistical artificial neural network was used for quantifying subjective data, and soil moisture layouts were produced by utilizing the ordinary kriging (OK) method. For cross-validating, the land surface temperature data from the MODIS satellite were retrieved. A platform for the region with 200 m grids resolution to collect daily soil moisture at eight ungauged stations is proposed to utilize subjective data from the subscribers and cross-validated with satellite data. A virtual station at the centroid of the pervious part of the study area was selected as a reference station for data collection daily or weekly to generate soil moisture time series. The results showed a high potential of utilizing satellite and citizen science data for real-time estimation of scarce soil moisture data in developing regions.
Highlights
Soil moisture is one of the essential components of climate, ecological, and the hydrological analysis and modeling (MEA 2005) and is the best indicator of climate change (Karamouz et al 2019)
Data analysis The collected data was analyzed to study the distribution of the gravimetric soil moisture data and the users’ performance
The first sampling process results revealed an acceptable performance of subjective data with a high agreement among users in selecting soil moisture subjective classes
Summary
Soil moisture is one of the essential components of climate, ecological, and the hydrological analysis and modeling (MEA 2005) and is the best indicator of climate change (Karamouz et al 2019). It plays a vital role in exchanging water, energy, and carbon between soil and air, even though the unsaturated soil’s water content is less than 0.15% of the total global available freshwater (Dobriyal et al 2012). Satellite soil moisture data have more potential for large-scale modeling. These data have high temporal variability, their spatial resolution is low. They tried to establish a relationship between soil moisture content over two depths of 0–6 and 0–30 centimeters
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