Soil moisture data using citizen science technology cross-validated by satellite data

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mohammad Karamouz, Elham Ebrahimi, Arash Ghomlaghi
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	citizen science data-driven modeling image processing kriging method soil moisture Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/e3f7797195e74aed965f096545862368
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e3f7797195e74aed965f096545862368
record_format	dspace
spelling	oai:doaj.org-article:e3f7797195e74aed965f0965458623682021-11-23T18:48:39ZSoil moisture data using citizen science technology cross-validated by satellite data1464-71411465-173410.2166/hydro.2021.029https://doaj.org/article/e3f7797195e74aed965f0965458623682021-11-01T00:00:00Zhttp://jh.iwaponline.com/content/23/6/1224https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734Soil 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 Design of a platform for real-time data estimation of SM in virtual station(s) using color contrast image processing with simple user interface for retrieving SM data through social media.; Cross-validation and error analysis with daily downscaled satellite SM data provides a unique opportunity for SM estimation in the developing regions with no national or regional plan to collect time series of this data.;Mohammad KaramouzElham EbrahimiArash GhomlaghiIWA Publishingarticlecitizen sciencedata-driven modelingimage processingkriging methodsoil moistureInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 6, Pp 1224-1246 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	citizen science data-driven modeling image processing kriging method soil moisture Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	citizen science data-driven modeling image processing kriging method soil moisture Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Mohammad Karamouz Elham Ebrahimi Arash Ghomlaghi Soil moisture data using citizen science technology cross-validated by satellite data
description	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 Design of a platform for real-time data estimation of SM in virtual station(s) using color contrast image processing with simple user interface for retrieving SM data through social media.; Cross-validation and error analysis with daily downscaled satellite SM data provides a unique opportunity for SM estimation in the developing regions with no national or regional plan to collect time series of this data.;
format	article
author	Mohammad Karamouz Elham Ebrahimi Arash Ghomlaghi
author_facet	Mohammad Karamouz Elham Ebrahimi Arash Ghomlaghi
author_sort	Mohammad Karamouz
title	Soil moisture data using citizen science technology cross-validated by satellite data
title_short	Soil moisture data using citizen science technology cross-validated by satellite data
title_full	Soil moisture data using citizen science technology cross-validated by satellite data
title_fullStr	Soil moisture data using citizen science technology cross-validated by satellite data
title_full_unstemmed	Soil moisture data using citizen science technology cross-validated by satellite data
title_sort	soil moisture data using citizen science technology cross-validated by satellite data
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/e3f7797195e74aed965f096545862368
work_keys_str_mv	AT mohammadkaramouz soilmoisturedatausingcitizensciencetechnologycrossvalidatedbysatellitedata AT elhamebrahimi soilmoisturedatausingcitizensciencetechnologycrossvalidatedbysatellitedata AT arashghomlaghi soilmoisturedatausingcitizensciencetechnologycrossvalidatedbysatellitedata
_version_	1718416186460340224

Soil moisture data using citizen science technology cross-validated by satellite data

Ejemplares similares